Asif Ali Khan |
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Phone Fax Visitor's Address |
+49 (0)351 463 463 43729 +49 (0)351 463 39995 Helmholtzstrasse 18, BAR III55 |
Asif Ali Khan received his Bachelor and Master degrees in Computer Systems Engineering from Pakistan in 2012 and 2015 respectively. He has a background in Computer Architecture, embedded systems and parallel processing. At the Chair for Compiler Construction, he is working on systems with heterogeneous memories. He aims to:
- Realize a multi-core architecture with heterogeneous memories that meets the multi-faceted memory requirements (such as bandwidth, latency, energy etc) of various applications.
- Expose this memory heterogeneity to the application programmer by providing language and compilation support.
I work on the intersection of optimizing compilers and emerging memory/compute architectures. If you are interested to do your Bachelor/Master/Diploma thesis in these domains, I could help you in supervising topics similar to the following. In case you have a different topic in mind that is related, please feel free to reach out and we will talk about it.
- Compilation for unconventional architectures:
Emerging non-volatile memory technologies promise to solve the capacity, latency, and energy issues of conventional architecture. They, however, have their own limitations. In this project, you will work on developing/extending a compiler that hides/mitigate these limitations and exploit the full potential of these novel architectures. As a target system, you will work on systems with Racetrack Memories (don't worry if you do not have knowledge of the technology itself).
RTMs are sequential. New compilers are needed to minimize the latency of sequential accesses and improve performance.
Requirements: Good knowledge of C/C++
Beneficial: basic compiler knowledge, LLVM
Related work: A recent thesis (MA) and a similar publication
- Optimizing Data Movement in the Memory Hierarchy:
Requirements: Good knowledge of C/C++
In case of interest do not hesitate to contact me: asif_ali.khan@tu-dresden.de
2022
- Carlos Escuin, Asif Ali Khan, Pablo Ibañez, Teresa Monreal, Victor Viñals, Jeronimo Castrillon, "HyCSim: A Rapid Design Space Exploration Tool for Emerging Hybrid Last-Level Caches", In Proceeding: DroneSE and RAPIDO: System Engineering for Constrained Embedded Systems, co-located with the 17th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC), Association for Computing Machinery, pp. 53–58, New York, NY, USA, Jun 2022. [doi] [Bibtex & Downloads]
HyCSim: A Rapid Design Space Exploration Tool for Emerging Hybrid Last-Level Caches
×Reference
Carlos Escuin, Asif Ali Khan, Pablo Ibañez, Teresa Monreal, Victor Viñals, Jeronimo Castrillon, "HyCSim: A Rapid Design Space Exploration Tool for Emerging Hybrid Last-Level Caches", In Proceeding: DroneSE and RAPIDO: System Engineering for Constrained Embedded Systems, co-located with the 17th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC), Association for Computing Machinery, pp. 53–58, New York, NY, USA, Jun 2022. [doi]
Abstract
Recent years have seen a rising trend in the exploration of non- volatile memory (NVM) technologies in the memory subsystem. Particularly in the cache hierarchy, hybrid last-level cache (LLC) solutions are proposed to meet the wide-ranging performance and energy requirements of modern days applications. These emerging hybrid solutions need simulation and detailed exploration to fully understand their capabilities before exploiting them. Existing simulation tools are either too slow or incapable of prototyping such systems and optimizing for NVM devices. To this end, we propose HyCSim, a trace-driven simulation infrastructure that enables rapid comparison of various hybrid LLC configurations for different optimization objectives. Notably, HyCSim makes it possible to quickly estimate the impact of various hybrid LLC insertion and replacement policies, disabling of a cache region at byte or cache frame granularity for different fault maps. In addition, HyCSim allows to evaluate the impact of various compression schemes on the overall performance (hit and miss rate) and the number of writes to the LLC. Our evaluation on ten multi-program workloads from the SPEC 2006 benchmarks suite shows that HyCSim accelerates the simulation time by 24x, compared to the cycle-accurate Gem5 simulator, with high-fidelity.
Bibtex
@InProceedings{escuin_rapido22,
author = {Carlos Escuin and Asif Ali Khan and Pablo Iba{\~n}ez and Teresa Monreal and Victor Vi{\~n}als and Jeronimo Castrillon},
booktitle = {DroneSE and RAPIDO: System Engineering for Constrained Embedded Systems, co-located with the 17th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC)},
date = {2022-06},
title = {HyCSim: A Rapid Design Space Exploration Tool for Emerging Hybrid Last-Level Caches},
doi = {10.1145/3522784.3522801},
isbn = {9781450395663},
location = {Budapest, Hungary},
pages = {53–58},
publisher = {Association for Computing Machinery},
series = {DroneSE and RAPIDO '22},
url = {https://doi.org/10.1145/3522784.3522801},
abstract = {Recent years have seen a rising trend in the exploration of non- volatile memory (NVM) technologies in the memory subsystem. Particularly in the cache hierarchy, hybrid last-level cache (LLC) solutions are proposed to meet the wide-ranging performance and energy requirements of modern days applications. These emerging hybrid solutions need simulation and detailed exploration to fully understand their capabilities before exploiting them. Existing simulation tools are either too slow or incapable of prototyping such systems and optimizing for NVM devices. To this end, we propose HyCSim, a trace-driven simulation infrastructure that enables rapid comparison of various hybrid LLC configurations for different optimization objectives. Notably, HyCSim makes it possible to quickly estimate the impact of various hybrid LLC insertion and replacement policies, disabling of a cache region at byte or cache frame granularity for different fault maps. In addition, HyCSim allows to evaluate the impact of various compression schemes on the overall performance (hit and miss rate) and the number of writes to the LLC. Our evaluation on ten multi-program workloads from the SPEC 2006 benchmarks suite shows that HyCSim accelerates the simulation time by 24x, compared to the cycle-accurate Gem5 simulator, with high-fidelity.},
address = {New York, NY, USA},
month = jun,
numpages = {6},
year = {2022},
}Downloads
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- Asif Ali Khan, "Design and Code Optimization for Systems with Next-generation Racetrack Memories", PhD thesis, TU Dresden, 255 pp., Apr 2022. [Bibtex & Downloads]
Design and Code Optimization for Systems with Next-generation Racetrack Memories
×Reference
Asif Ali Khan, "Design and Code Optimization for Systems with Next-generation Racetrack Memories", PhD thesis, TU Dresden, 255 pp., Apr 2022.
Bibtex
@PhdThesis{Khan_PhD,
author = {Khan, Asif Ali},
institution = {TU Dresden},
title = {Design and Code Optimization for Systems with Next-generation Racetrack Memories},
pages = {255 pp.},
month = apr,
year = {2022},
url = {https://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa2-795738},
}Downloads
2204_Khan_PhD [PDF]
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- Asif Ali Khan, Sebastien Ollivier, Stephen Longofono, Gerald Hempel, Jeronimo Castrillon, Alex K. Jones, "Brain-inspired Cognition in Next Generation Racetrack Memories", In ACM Transactions on Embedded Computing Systems (TECS), Association for Computing Machinery, New York, NY, USA, Mar 2022. [doi] [Bibtex & Downloads]
Brain-inspired Cognition in Next Generation Racetrack Memories
×Reference
Asif Ali Khan, Sebastien Ollivier, Stephen Longofono, Gerald Hempel, Jeronimo Castrillon, Alex K. Jones, "Brain-inspired Cognition in Next Generation Racetrack Memories", In ACM Transactions on Embedded Computing Systems (TECS), Association for Computing Machinery, New York, NY, USA, Mar 2022. [doi]
Abstract
Hyperdimensional computing (HDC) is an emerging computational framework inspired by the brain that operates on vectors with thousands of dimensions to emulate cognition. Unlike conventional computational frameworks that operate on numbers, HDC, like the brain, uses high dimensional random vectors and is capable of one-shot learning. HDC is based on a well-defined set of arithmetic operations and is highly error-resilient. The core operations of HDC manipulate HD vectors in bulk bit-wise fashion, offering many opportunities to leverage parallelism. Unfortunately, on conventional von Neumann architectures, the continuous movement of HD vectors among the processor and the memory can make the cognition task prohibitively slow and energy-intensive. Hardware accelerators only marginally improve related metrics. In contrast, even partial implementations of an HDC framework inside memory can provide considerable performance/energy gains as demonstrated in prior work using memristors. This paper presents an architecture based on racetrack memory (RTM) to conduct and accelerate the entire HDC framework within memory. The proposed solution requires minimal additional CMOS circuitry by leveraging a read operation across multiple domains in RTMs called transverse read (TR) to realize exclusive-or (XOR) and addition operations. To minimize the CMOS circuitry overhead, an RTM nanowire-based counting mechanism is proposed. Using language recognition as the example workload, the proposed RTM HDC system reduces the energy consumption by 8.6x compared to the state-of-the-art in-memory implementation. Compared to dedicated hardware design realized with an FPGA, RTM-based HDC processing demonstrates 7.8x and 5.3x improvements in the overall runtime and energy consumption, respectively.
Bibtex
@Article{khan_tecs22,
author = {Asif Ali Khan and Sebastien Ollivier and Stephen Longofono and Gerald Hempel and Jeronimo Castrillon and Alex K. Jones},
title = {Brain-inspired Cognition in Next Generation Racetrack Memories},
abstract = {Hyperdimensional computing (HDC) is an emerging computational framework inspired by the brain that operates on vectors with thousands of dimensions to emulate cognition. Unlike conventional computational frameworks that operate on numbers, HDC, like the brain, uses high dimensional random vectors and is capable of one-shot learning. HDC is based on a well-defined set of arithmetic operations and is highly error-resilient. The core operations of HDC manipulate HD vectors in bulk bit-wise fashion, offering many opportunities to leverage parallelism. Unfortunately, on conventional von Neumann architectures, the continuous movement of HD vectors among the processor and the memory can make the cognition task prohibitively slow and energy-intensive. Hardware accelerators only marginally improve related metrics. In contrast, even partial implementations of an HDC framework inside memory can provide considerable performance/energy gains as demonstrated in prior work using memristors. This paper presents an architecture based on racetrack memory (RTM) to conduct and accelerate the entire HDC framework within memory. The proposed solution requires minimal additional CMOS circuitry by leveraging a read operation across multiple domains in RTMs called transverse read (TR) to realize exclusive-or (XOR) and addition operations. To minimize the CMOS circuitry overhead, an RTM nanowire-based counting mechanism is proposed. Using language recognition as the example workload, the proposed RTM HDC system reduces the energy consumption by 8.6x compared to the state-of-the-art in-memory implementation. Compared to dedicated hardware design realized with an FPGA, RTM-based HDC processing demonstrates 7.8x and 5.3x improvements in the overall runtime and energy consumption, respectively.},
address = {New York, NY, USA},
journal = {ACM Transactions on Embedded Computing Systems (TECS)},
month = mar,
numpages = {28},
publisher = {Association for Computing Machinery},
year = {2022},
doi = {10.1145/3524071},
issn = {1539-9087},
url = {https://doi.org/10.1145/3524071},
}Downloads
2203_Khan_TECS [PDF]
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2021
- Joonas Iisakki Multanen, Kari Hepola, Asif Ali Khan, Jeronimo Castrillon, Pekka Jääskeläinen, "Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory", In IEEE Transactions on Computers, pp. 1-1, Oct 2021. [doi] [Bibtex & Downloads]
Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory
×Reference
Joonas Iisakki Multanen, Kari Hepola, Asif Ali Khan, Jeronimo Castrillon, Pekka Jääskeläinen, "Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory", In IEEE Transactions on Computers, pp. 1-1, Oct 2021. [doi]
Bibtex
@Article{multanen_toc21,
author = {Joonas Iisakki Multanen and Kari Hepola and Asif Ali Khan and Jeronimo Castrillon and Pekka J{\"a}{\"a}skel{\"a}inen},
title = {Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory},
doi = {10.1109/TC.2021.3117439},
url = {https://ieeexplore.ieee.org/document/9557799},
pages = {1-1},
journal = {IEEE Transactions on Computers},
year = {2021},
month = oct,
}Downloads
2110_Multanen_TOC [PDF]
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- Adam Siemieniuk, Lorenzo Chelini, Asif Ali Khan, Jeronimo Castrillon, Andi Drebes, Henk Corporaal, Tobias Grosser, Martin Kong, "OCC: An Automated End-to-End Machine Learning Optimizing Compiler for Computing-In-Memory", In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), IEEE Press, vol. 41, no. 6, pp. 1674-1686, Aug 2021. [doi] [Bibtex & Downloads]
OCC: An Automated End-to-End Machine Learning Optimizing Compiler for Computing-In-Memory
×Reference
Adam Siemieniuk, Lorenzo Chelini, Asif Ali Khan, Jeronimo Castrillon, Andi Drebes, Henk Corporaal, Tobias Grosser, Martin Kong, "OCC: An Automated End-to-End Machine Learning Optimizing Compiler for Computing-In-Memory", In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), IEEE Press, vol. 41, no. 6, pp. 1674-1686, Aug 2021. [doi]
Bibtex
@Article{khan_tcad21,
author = {Adam Siemieniuk and Lorenzo Chelini and Asif Ali Khan and Jeronimo Castrillon and Andi Drebes and Henk Corporaal and Tobias Grosser and Martin Kong},
journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD)},
title = {{OCC}: An Automated End-to-End Machine Learning Optimizing Compiler for Computing-In-Memory},
month = aug,
volume={41},
number={6},
pages={1674-1686},
numpages = {12 pp},
doi = {10.1109/TCAD.2021.3101464},
issn = {1937-4151},
url = {https://ieeexplore.ieee.org/document/9502921},
publisher = {IEEE Press},
year = {2021},
}Downloads
2107_Khan_TCAD [PDF]
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- Christian Hakert, Asif Ali Khan, Kuan-Hsun Chen, Fazal Hameed, Jeronimo Castrillon, Jian-Jia Chen, "BLOwing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory", Proceedings of the 58th Annual Design Automation Conference (DAC'21), ACM, pp. 1111-1116, Jul 2021. [doi] [Bibtex & Downloads]
BLOwing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory
×Reference
Christian Hakert, Asif Ali Khan, Kuan-Hsun Chen, Fazal Hameed, Jeronimo Castrillon, Jian-Jia Chen, "BLOwing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory", Proceedings of the 58th Annual Design Automation Conference (DAC'21), ACM, pp. 1111-1116, Jul 2021. [doi]
Bibtex
@InProceedings{khan_dac21,
author = {Christian Hakert and Asif Ali Khan and Kuan-Hsun Chen and Fazal Hameed and Jeronimo Castrillon and Jian-Jia Chen},
booktitle = {Proceedings of the 58th Annual Design Automation Conference (DAC'21)},
title = {{BLO}wing Trees to the Ground: Layout Optimization of Decision Trees on Racetrack Memory},
doi = {10.1109/DAC18074.2021.9586167},
location = {San Francisco, California},
pages = {1111-1116},
series = {DAC '21},
url = {https://ieeexplore.ieee.org/document/9586167},
publisher = {ACM},
month = jul,
year = {2021},
}Downloads
2112_Hakert_DAC [PDF]
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- Fazal Hameed, Asif Ali Khan, Jeronimo Castrillon, "ALPHA: A Novel Algorithm-Hardware Co-design for Accelerating DNA Seed Location Filtering", In IEEE Transactions on Emerging Topics in Computing (IEEE TETC), 12 pp., Jun 2021. [doi] [Bibtex & Downloads]
ALPHA: A Novel Algorithm-Hardware Co-design for Accelerating DNA Seed Location Filtering
×Reference
Fazal Hameed, Asif Ali Khan, Jeronimo Castrillon, "ALPHA: A Novel Algorithm-Hardware Co-design for Accelerating DNA Seed Location Filtering", In IEEE Transactions on Emerging Topics in Computing (IEEE TETC), 12 pp., Jun 2021. [doi]
Abstract
Sequence alignment is a fundamental operation in genomic analysis where DNA fragments called reads are mapped to a long reference DNA sequence. There exist a number of (in)exact alignment algorithms with varying sensitivity for both local and global alignments, however, they are all computationally expensive. With the advent of high-throughput sequencing (HTS) technologies that generate a mammoth amount of data, there is increased pressure on improving the performance and capacity of the analysis algorithms in general and the mapping algorithms in particular. While many works focus on improving the performance of the aligner themselves, recently it has been demonstrated that restricting the mapping space for input reads and filtering out mapping positions that will result in a poor match can significantly improve the performance of the alignment operation. However, this is only true if it is guaranteed that the filtering operation can be performed significantly faster. Otherwise, it can easily outweigh the benefits of the aligner. To expedite this pre-alignment filtering, among others, the recently proposed GRIM-Filter uses highly-parallel processing-in-memory operations benefiting from light-weight computational units on the logic-in-memory layer. However, the significant amount of data transferring between the memory and logic-in-memory layers quickly becomes a performance and energy bottleneck for the memory subsystem and ultimately for the overall system. By analyzing input genomes, we found that there are unexpected data-reuse opportunities in the filtering operation. We propose an algorithm-hardware co-design that exploits the data-reuse in the seed location filtering operation and, compared to the GRIM-Filter, cuts the number of memory accesses by 22-54%. This reduction in memory accesses improves the overall performance and energy consumption by 19-44% and 21-49%, respectively.
Bibtex
@Article{hameed_tetc21,
author = {Fazal Hameed and Asif Ali Khan and Jeronimo Castrillon},
journal = {IEEE Transactions on Emerging Topics in Computing (IEEE TETC)},
title = {{ALPHA}: A Novel Algorithm-Hardware Co-design for Accelerating {DNA} Seed Location Filtering},
pages = {12 pp.},
abstract = {Sequence alignment is a fundamental operation in genomic analysis where DNA fragments called reads are mapped to a long reference DNA sequence. There exist a number of (in)exact alignment algorithms with varying sensitivity for both local and global alignments, however, they are all computationally expensive. With the advent of high-throughput sequencing (HTS) technologies that generate a mammoth amount of data, there is increased pressure on improving the performance and capacity of the analysis algorithms in general and the mapping algorithms in particular. While many works focus on improving the performance of the aligner themselves, recently it has been demonstrated that restricting the mapping space for input reads and filtering out mapping positions that will result in a poor match can significantly improve the performance of the alignment operation. However, this is only true if it is guaranteed that the filtering operation can be performed significantly faster. Otherwise, it can easily outweigh the benefits of the aligner. To expedite this pre-alignment filtering, among others, the recently proposed GRIM-Filter uses highly-parallel processing-in-memory operations benefiting from light-weight computational units on the logic-in-memory layer. However, the significant amount of data transferring between the memory and logic-in-memory layers quickly becomes a performance and energy bottleneck for the memory subsystem and ultimately for the overall system. By analyzing input genomes, we found that there are unexpected data-reuse opportunities in the filtering operation. We propose an algorithm-hardware co-design that exploits the data-reuse in the seed location filtering operation and, compared to the GRIM-Filter, cuts the number of memory accesses by 22-54\%. This reduction in memory accesses improves the overall performance and energy consumption by 19-44\% and 21-49\%, respectively.},
month = jun,
year = {2021},
doi = {10.1109/TETC.2021.3093840},
issn = {2168-6750},
}Downloads
2107_hameed_TETC [PDF]
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- Joonas Iisakki Multanen, Kari Hepola, Asif Ali Khan, Jeronimo Castrillon, Pekka Jaaskelainen, "Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory", In IEEE Transactions on Computers, Institute of Electrical and Electronics Engineers (IEEE), pp. 1–1, 2021. [doi] [Bibtex & Downloads]
Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory
×Reference
Joonas Iisakki Multanen, Kari Hepola, Asif Ali Khan, Jeronimo Castrillon, Pekka Jaaskelainen, "Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory", In IEEE Transactions on Computers, Institute of Electrical and Electronics Engineers (IEEE), pp. 1–1, 2021. [doi]
Bibtex
@article{Multanen_2021,
doi = {10.1109/tc.2021.3117439},
url = {https://doi.org/10.1109%2Ftc.2021.3117439},
year = 2021,
publisher = {Institute of Electrical and Electronics Engineers ({IEEE})},
pages = {1--1},
author = {Joonas Iisakki Multanen and Kari Hepola and Asif Ali Khan and Jeronimo Castrillon and Pekka Jaaskelainen},
title = {Energy-Efficient Instruction Delivery in Embedded Systems with Domain Wall Memory},
journal = {{IEEE} Transactions on Computers}
}Downloads
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2020
- Asif Ali Khan, Hauke Mewes, Tobias Grosser, Torsten Hoefler, Jeronimo Castrillon, "Polyhedral Compilation for Racetrack Memories", In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). Special issue on Compilers, Architecture, and Synthesis of Embedded Systems (CASES'20), IEEE Press, vol. 39, no. 11, pp. 3968–3980, Oct 2020. [doi] [Bibtex & Downloads]
Polyhedral Compilation for Racetrack Memories
×Reference
Asif Ali Khan, Hauke Mewes, Tobias Grosser, Torsten Hoefler, Jeronimo Castrillon, "Polyhedral Compilation for Racetrack Memories", In IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). Special issue on Compilers, Architecture, and Synthesis of Embedded Systems (CASES'20), IEEE Press, vol. 39, no. 11, pp. 3968–3980, Oct 2020. [doi]
Abstract
Traditional memory hierarchy designs, primarily based on SRAM and DRAM, become increasingly unsuitable to meet the performance, energy, bandwidth and area requirements of modern embedded and high-performance computer systems. Racetrack Memory (RTM), an emerging non-volatile memory technology, promises to meet these conflicting demands by offering simultaneously high speed, higher density, and non-volatility. RTM provides these efficiency gains by not providing immediate access to all storage locations, but by instead storing data sequentially in the equivalent to nanoscale tapes called tracks. Before any data can be accessed, explicit shift operations must be issued that cost energy and increase access latency. The result is a fundamental change in memory performance behavior: the address distance between subsequent memory accesses now has a linear effect on memory performance. While there are first techniques to optimize programs for linear-latency memories such as RTM, existing automatic solutions treat only scalar memory accesses. This work presents the first automatic compilation framework that optimizes static loop programs over arrays for linear-latency memories. We extend the polyhedral compilation framework Polly to generate code that maximizes accesses to the same or consecutive locations, thereby minimizing the number of shifts. Our experimental results show that the optimized code incurs up to 85% fewer shifts (average 41%), improving both performance and energy consumption by an average of 17.9% and 39.8%, respectively. Our results show that automatic techniques make it possible to effectively program linear-latency memory architectures such as RTM.
Bibtex
@Article{khan_cases20,
author = {Asif Ali Khan and Hauke Mewes and Tobias Grosser and Torsten Hoefler and Jeronimo Castrillon},
title = {Polyhedral Compilation for Racetrack Memories},
journal = {IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD). Special issue on Compilers, Architecture, and Synthesis of Embedded Systems (CASES'20)},
year = {2020},
series = {CASES ’20},
month = oct,
doi = {10.1109/TCAD.2020.3012266},
url = {https://ieeexplore.ieee.org/document/9216560},
volume={39},
number={11},
pages={3968--3980},
issn = {1937-4151},
issn = {1937-4151},
abstract = {Traditional memory hierarchy designs, primarily based on SRAM and DRAM, become increasingly unsuitable to meet the performance, energy, bandwidth and area requirements of modern embedded and high-performance computer systems. Racetrack Memory (RTM), an emerging non-volatile memory technology, promises to meet these conflicting demands by offering simultaneously high speed, higher density, and non-volatility. RTM provides these efficiency gains by not providing immediate access to all storage locations, but by instead storing data sequentially in the equivalent to nanoscale tapes called tracks. Before any data can be accessed, explicit shift operations must be issued that cost energy and increase access latency. The result is a fundamental change in memory performance behavior: the address distance between subsequent memory accesses now has a linear effect on memory performance. While there are first techniques to optimize programs for linear-latency memories such as RTM, existing automatic solutions treat only scalar memory accesses. This work presents the first automatic compilation framework that optimizes static loop programs over arrays for linear-latency memories. We extend the polyhedral compilation framework Polly to generate code that maximizes accesses to the same or consecutive locations, thereby minimizing the number of shifts. Our experimental results show that the optimized code incurs up to 85\% fewer shifts (average 41\%), improving both performance and energy consumption by an average of 17.9\% and 39.8\%, respectively. Our results show that automatic techniques make it possible to effectively program linear-latency memory architectures such as RTM.},
booktitle = {Proceedings of the 2020 International Conference on Compilers, Architecture, and Synthesis of Embedded Systems (CASES)},
location = {Virtual conference},
numpages = {12},
publisher = {IEEE Press},
}Downloads
2009_Khan_CASES [PDF]
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- Fazal Hameed, Asif Ali Khan, Jeronimo Castrillon, "Improving the Performance of Block-based DRAM Caches via Tag-Data Decoupling", In IEEE Transactions on Computers, vol. 70, no. 11, pp. 1914-1927, Oct 2020. [doi] [Bibtex & Downloads]
Improving the Performance of Block-based DRAM Caches via Tag-Data Decoupling
×Reference
Fazal Hameed, Asif Ali Khan, Jeronimo Castrillon, "Improving the Performance of Block-based DRAM Caches via Tag-Data Decoupling", In IEEE Transactions on Computers, vol. 70, no. 11, pp. 1914-1927, Oct 2020. [doi]
Abstract
In-package DRAM-based Last-Level-Caches (LLCs) that cache data in small chunks (i.e., blocks) are promising for improving system performance due to their efficient main memory bandwidth utilization. However, in these high-capacity DRAM caches, managing metadata (i.e., tags) at low cost is challenging. Storing the tags in SRAM has the advantage of quick tag access but is impractical due to a large area overhead. Storing the tags in DRAM reduces the area overhead but incurs tag serialization latency for an associative LLC design, which is inevitable for achieving high cache hit rate. To address the area and latency overhead problem, we propose a block- based DRAM LLC design that decouples tag and data into two regions in DRAM. Our design stores the tags in a latency-optimized DRAM region as the tags are accessed more often than the data. In contrast, we optimize the data region for area efficiency and map spatially-adjacent cache blocks to the same DRAM row to exploit spatial locality. Our design mitigates the tag serialization latency of existing associative DRAM LLCs via selective in-DRAM tag comparison, which overlaps the latency of tag and data accesses. This efficiently enables LLC bypassing via a novel DRAM Absence Table (DAT) that not only provides fast LLC miss detection but also reduces in-package bandwidth requirements. Our evaluation using SPEC2006 benchmarks shows that our tag-data decoupled LLC improves system performance by 11.7% compared to a state-of-the-art direct-mapped LLC design and by 7.2% compared to an existing associative LLC design.
Bibtex
@Article{hameed_tc20,
author = {Fazal Hameed and Asif Ali Khan and Jeronimo Castrillon},
title = {Improving the Performance of Block-based DRAM Caches via Tag-Data Decoupling},
journal = {IEEE Transactions on Computers},
year = {2020},
month = oct,
abstract = {In-package DRAM-based Last-Level-Caches (LLCs) that cache data in small chunks (i.e., blocks) are promising for improving system performance due to their efficient main memory bandwidth utilization. However, in these high-capacity DRAM caches, managing metadata (i.e., tags) at low cost is challenging. Storing the tags in SRAM has the advantage of quick tag access but is impractical due to a large area overhead. Storing the tags in DRAM reduces the area overhead but incurs tag serialization latency for an associative LLC design, which is inevitable for achieving high cache hit rate. To address the area and latency overhead problem, we propose a block- based DRAM LLC design that decouples tag and data into two regions in DRAM. Our design stores the tags in a latency-optimized DRAM region as the tags are accessed more often than the data. In contrast, we optimize the data region for area efficiency and map spatially-adjacent cache blocks to the same DRAM row to exploit spatial locality. Our design mitigates the tag serialization latency of existing associative DRAM LLCs via selective in-DRAM tag comparison, which overlaps the latency of tag and data accesses. This efficiently enables LLC bypassing via a novel DRAM Absence Table (DAT) that not only provides fast LLC miss detection but also reduces in-package bandwidth requirements. Our evaluation using SPEC2006 benchmarks shows that our tag-data decoupled LLC improves system performance by 11.7\% compared to a state-of-the-art direct-mapped LLC design and by 7.2\% compared to an existing associative LLC design.},
doi = {10.1109/TC.2020.3029615},
url = {https://ieeexplore.ieee.org/document/9220805},
issn = {0018-9340},
numpages = {14},
volume={70},
number={11},
pages={1914-1927},
}Downloads
2010_Hameed_TC [PDF]
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- Asif Ali Khan, Norman A. Rink, Fazal Hameed, Jeronimo Castrillon, "Optimizing Tensor Contractions for Embedded Devices with Racetrack and DRAM Memories", In ACM Transactions on Embedded Computing Systems (TECS), Association for Computing Machinery, vol. 19, no. 6, New York, NY, USA, Sep 2020. [doi] [Bibtex & Downloads]
Optimizing Tensor Contractions for Embedded Devices with Racetrack and DRAM Memories
×Reference
Asif Ali Khan, Norman A. Rink, Fazal Hameed, Jeronimo Castrillon, "Optimizing Tensor Contractions for Embedded Devices with Racetrack and DRAM Memories", In ACM Transactions on Embedded Computing Systems (TECS), Association for Computing Machinery, vol. 19, no. 6, New York, NY, USA, Sep 2020. [doi]
Abstract
Tensor contraction is a fundamental operation in many algorithms with a plethora of applications ranging from quantum chemistry over fluid dynamics and image processing to machine learning. The performance of tensor computations critically depends on the efficient utilization of on-chip/off-chip memories. In the context of low-power embedded devices, efficient management of the memory space becomes even more crucial, in order to meet energy constraints. This work aims at investigating strategies for performance- and energy-efficient tensor contractions on embedded systems, using racetrack memory (RTM)-based scratch-pad memory (SPM) and DRAM-based off-chip memory. Compiler optimizations such as the loop access order and data layout transformations paired with architectural optimizations such as prefetching and preshifting are employed to reduce the shifting overhead in RTMs. Optimizations for off-chip memory such as memory access order, data mapping and the choice of a suitable memory access granularity are employed to reduce the contention in the off-chip memory. Experimental results demonstrate that the proposed optimizations improve the SPM performance and energy consumption by 32% and 73% respectively compared to an iso-capacity SRAM. The overall DRAM dynamic energy consumption improvements due to memory optimizations amount to 80%.
Bibtex
@Article{khan_tecs20,
author = {Asif Ali Khan and Norman A. Rink and Fazal Hameed and Jeronimo Castrillon},
title = {Optimizing Tensor Contractions for Embedded Devices with Racetrack and DRAM Memories},
journal = {ACM Transactions on Embedded Computing Systems (TECS)},
year = {2020},
month = sep,
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {19},
number = {6},
issn = {1539-9087},
url = {https://doi.org/10.1145/3396235},
doi = {10.1145/3396235},
articleno = {44},
numpages = {26},
abstract = {Tensor contraction is a fundamental operation in many algorithms with a plethora of applications ranging from quantum chemistry over fluid dynamics and image processing to machine learning. The performance of tensor computations critically depends on the efficient utilization of on-chip/off-chip memories. In the context of low-power embedded devices, efficient management of the memory space becomes even more crucial, in order to meet energy constraints. This work aims at investigating strategies for performance- and energy-efficient tensor contractions on embedded systems, using racetrack memory (RTM)-based scratch-pad memory (SPM) and DRAM-based off-chip memory. Compiler optimizations such as the loop access order and data layout transformations paired with architectural optimizations such as prefetching and preshifting are employed to reduce the shifting overhead in RTMs. Optimizations for off-chip memory such as memory access order, data mapping and the choice of a suitable memory access granularity are employed to reduce the contention in the off-chip memory. Experimental results demonstrate that the proposed optimizations improve the SPM performance and energy consumption by 32\% and 73\% respectively compared to an iso-capacity SRAM. The overall DRAM dynamic energy consumption improvements due to memory optimizations amount to 80\%.},
}Downloads
2009_Khan_TECS [PDF]
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- Robin Bläsing, Asif Ali Khan, Panagiotis Ch. Filippou, Chirag Garg, Fazal Hameed, Jeronimo Castrillon, Stuart S. P. Parkin, "Magnetic Racetrack Memory: From Physics to the Cusp of Applications within a Decade", In Proceedings of the IEEE, vol. 108, no. 8, pp. 1303-1321, Mar 2020. [doi] [Bibtex & Downloads]
Magnetic Racetrack Memory: From Physics to the Cusp of Applications within a Decade
×Reference
Robin Bläsing, Asif Ali Khan, Panagiotis Ch. Filippou, Chirag Garg, Fazal Hameed, Jeronimo Castrillon, Stuart S. P. Parkin, "Magnetic Racetrack Memory: From Physics to the Cusp of Applications within a Decade", In Proceedings of the IEEE, vol. 108, no. 8, pp. 1303-1321, Mar 2020. [doi]
Bibtex
@Article{khan_pieee20,
author = {Robin Bl{\"a}sing and Asif Ali Khan and Panagiotis Ch. Filippou and Chirag Garg and Fazal Hameed and Jeronimo Castrillon and Stuart S. P. Parkin},
title = {Magnetic Racetrack Memory: From Physics to the Cusp of Applications within a Decade},
journal = {Proceedings of the IEEE},
year = {2020},
month = mar,
volume={108},
number={8},
pages={1303-1321},
doi = {10.1109/JPROC.2020.2975719},
url = {https://ieeexplore.ieee.org/document/9045991},
}Downloads
2003_Khan_JPROC [PDF]
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- Asif Ali Khan, Andrés Goens, Fazal Hameed, Jeronimo Castrillon, "Generalized Data Placement Strategies for Racetrack Memories", Proceedings of the 2020 Design, Automation and Test in Europe Conference (DATE), IEEE, pp. 1502–1507, Mar 2020. (Video Presentation) [doi] [Bibtex & Downloads]
Generalized Data Placement Strategies for Racetrack Memories
×Reference
Asif Ali Khan, Andrés Goens, Fazal Hameed, Jeronimo Castrillon, "Generalized Data Placement Strategies for Racetrack Memories", Proceedings of the 2020 Design, Automation and Test in Europe Conference (DATE), IEEE, pp. 1502–1507, Mar 2020. (Video Presentation) [doi]
Abstract
Ultra-dense non-volatile racetrack memories (RTMs) have been investigated at various levels in the memory hierarchy for improved performance and reduced energy consumption. However, the innate shift operations in RTMs hinder their applicability to replace low-latency on-chip memories. Recent research has demonstrated that intelligent placement of memory objects in RTMs can significantly reduce the amount of shifts with no hardware overhead, albeit for specific system setups. However, existing placement strategies may lead to sub-optimal performance when applied to different architectures. In this paper we look at generalized data placement mechanisms that improve upon existing ones by taking into account the underlying memory architecture and the timing and liveliness information of memory objects. We propose a novel heuristic and a formulation using genetic algorithms that optimize key performance parameters. We show that, on average, our generalized approach improves the number of shifts, performance and energy consumption by 4.3x, 46% and 55% respectively compared to the state-of-the-art.
Bibtex
@InProceedings{khan_date20,
author = {Asif Ali Khan and Andr{\'e}s Goens and Fazal Hameed and Jeronimo Castrillon},
title = {Generalized Data Placement Strategies for Racetrack Memories},
booktitle = {Proceedings of the 2020 Design, Automation and Test in Europe Conference (DATE)},
year = {2020},
series = {DATE '20},
publisher = {IEEE},
location = {Grenoble, France},
month = mar,
isbn = {978-3-9819263-4-7},
pages = {1502--1507},
doi = {10.23919/DATE48585.2020.9116245},
url = {https://ieeexplore.ieee.org/document/9116245},
abstract = {Ultra-dense non-volatile racetrack memories (RTMs) have been investigated at various levels in the memory hierarchy for improved performance and reduced energy consumption. However, the innate shift operations in RTMs hinder their applicability to replace low-latency on-chip memories. Recent research has demonstrated that intelligent placement of memory objects in RTMs can significantly reduce the amount of shifts with no hardware overhead, albeit for specific system setups. However, existing placement strategies may lead to sub-optimal performance when applied to different architectures. In this paper we look at generalized data placement mechanisms that improve upon existing ones by taking into account the underlying memory architecture and the timing and liveliness information of memory objects. We propose a novel heuristic and a formulation using genetic algorithms that optimize key performance parameters. We show that, on average, our generalized approach improves the number of shifts, performance and energy consumption by 4.3x, 46% and 55% respectively compared to the state-of-the-art.},
}Downloads
2003_Khan_DATE [PDF]
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2019
- Asif Ali Khan, Fazal Hameed, Robin Bläsing, Stuart S. P. Parkin, Jeronimo Castrillon, "ShiftsReduce: Minimizing Shifts in Racetrack Memory 4.0", In ACM Transactions on Architecture and Code Optimization (TACO), ACM, vol. 16, no. 4, pp. 56:1–56:23, New York, NY, USA, Dec 2019. [doi] [Bibtex & Downloads]
ShiftsReduce: Minimizing Shifts in Racetrack Memory 4.0
×Reference
Asif Ali Khan, Fazal Hameed, Robin Bläsing, Stuart S. P. Parkin, Jeronimo Castrillon, "ShiftsReduce: Minimizing Shifts in Racetrack Memory 4.0", In ACM Transactions on Architecture and Code Optimization (TACO), ACM, vol. 16, no. 4, pp. 56:1–56:23, New York, NY, USA, Dec 2019. [doi]
Abstract
Racetrack memories (RMs) have significantly evolved since their conception in 2008, making them a serious contender in the field of emerging memory technologies. Despite key technological advancements, the access latency and energy consumption of an RM-based system are still highly influenced by the number of shift operations. These operations are required to move bits to the right positions in the racetracks. This paper presents data placement techniques for RMs that maximize the likelihood that consecutive references access nearby memory locations at runtime thereby minimizing the number of shifts. We present an integer linear programming (ILP) formulation for optimal data placement in RMs, and revisit existing offset assignment heuristics, originally proposed for random-access memories. We introduce a novel heuristic tailored to a realistic RM and combine it with a genetic search to further improve the solution. We show a reduction in the number of shifts of up to 52.5%, outperforming the state of the art by up to 16.1%.
Bibtex
@Article{khan_taco19,
author = {Asif Ali Khan and Fazal Hameed and Robin Bl{\"a}sing and Stuart S. P. Parkin and Jeronimo Castrillon},
title = {ShiftsReduce: Minimizing Shifts in Racetrack Memory 4.0},
journal = {ACM Transactions on Architecture and Code Optimization (TACO)},
issue_date = {December 2019},
volume = {16},
number = {4},
month = dec,
year = {2019},
issn = {1544-3566},
pages = {56:1--56:23},
articleno = {56},
numpages = {23},
url = {http://doi.acm.org/10.1145/3372489},
doi = {10.1145/3372489},
acmid = {3372489},
publisher = {ACM},
address = {New York, NY, USA},
abstract = {Racetrack memories (RMs) have significantly evolved since their conception in 2008, making them a serious contender in the field of emerging memory technologies. Despite key technological advancements, the access latency and energy consumption of an RM-based system are still highly influenced by the number of shift operations. These operations are required to move bits to the right positions in the racetracks. This paper presents data placement techniques for RMs that maximize the likelihood that consecutive references access nearby memory locations at runtime thereby minimizing the number of shifts. We present an integer linear programming (ILP) formulation for optimal data placement in RMs, and revisit existing offset assignment heuristics, originally proposed for random-access memories. We introduce a novel heuristic tailored to a realistic RM and combine it with a genetic search to further improve the solution. We show a reduction in the number of shifts of up to 52.5\%, outperforming the state of the art by up to 16.1\%.},
}Downloads
1912_Khan_TACO [PDF]
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- Joonas Multanen, Asif Ali Khan, Pekka Jääskeläinen, Fazal Hameed, Jeronimo Castrillon, "SHRIMP: Efficient Instruction Delivery with Domain Wall Memory", Proceedings of the International Symposium on Low Power Electronics and Design, ACM, 6pp, New York, NY, USA, Jul 2019. [doi] [Bibtex & Downloads]
SHRIMP: Efficient Instruction Delivery with Domain Wall Memory
×Reference
Joonas Multanen, Asif Ali Khan, Pekka Jääskeläinen, Fazal Hameed, Jeronimo Castrillon, "SHRIMP: Efficient Instruction Delivery with Domain Wall Memory", Proceedings of the International Symposium on Low Power Electronics and Design, ACM, 6pp, New York, NY, USA, Jul 2019. [doi]
Bibtex
@InProceedings{multanen_islped19,
author = {Joonas Multanen and Asif Ali Khan and Pekka J{\"a}{\"a}skel{\"a}inen and Fazal Hameed and Jeronimo Castrillon},
title = {{SHRIMP}: Efficient Instruction Delivery with Domain Wall Memory},
booktitle = {Proceedings of the International Symposium on Low Power Electronics and Design},
year = {2019},
month = jul,
series = {ISLPED '19},
location = {Lausanne, Switzerland},
pages = {6pp},
numpages = {6},
publisher = {ACM},
address = {New York, NY, USA},
doi={10.1109/ISLPED.2019.8824954},
url = {https://ieeexplore.ieee.org/document/8824954},
}Downloads
1907_Multanen_ISLPED [PDF]
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- Asif Ali Khan, Norman A. Rink, Fazal Hameed, Jeronimo Castrillon, "Optimizing Tensor Contractions for Embedded Devices with Racetrack Memory Scratch-Pads", Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, Tools and Theory of Embedded Systems (LCTES), ACM, pp. 5–18, New York, NY, USA, Jun 2019. [doi] [Bibtex & Downloads]
Optimizing Tensor Contractions for Embedded Devices with Racetrack Memory Scratch-Pads
×Reference
Asif Ali Khan, Norman A. Rink, Fazal Hameed, Jeronimo Castrillon, "Optimizing Tensor Contractions for Embedded Devices with Racetrack Memory Scratch-Pads", Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, Tools and Theory of Embedded Systems (LCTES), ACM, pp. 5–18, New York, NY, USA, Jun 2019. [doi]
Abstract
Tensor contraction is a fundamental operation in many algorithms with a plethora of applications ranging from quantum chemistry over fluid dynamics and image processing to machine learning. The performance of tensor computations critically depends on the efficient utilization of on-chip memories. In the context of low-power embedded devices, efficient management of the memory space becomes even more crucial, in order to meet energy constraints. This work aims at investigating strategies for performance- and energy-efficient tensor contractions on embedded systems, using racetrack memory (RTM)-based scratch-pad memory (SPM). Compiler optimizations such as the loop access order and data layout transformations paired with architectural optimizations such as prefetching and preshifting are employed to reduce the shifting overhead in RTMs. Experimental results demonstrate that the proposed optimizations improve the SPM performance and energy consumption by 24% and 74% respectively compared to an iso-capacity SRAM.
Bibtex
@InProceedings{kahn_lctes19,
author = {Asif Ali Khan and Norman A. Rink and Fazal Hameed and Jeronimo Castrillon},
title = {Optimizing Tensor Contractions for Embedded Devices with Racetrack Memory Scratch-Pads},
booktitle = {Proceedings of the 20th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, Tools and Theory of Embedded Systems (LCTES)},
series = {LCTES 2019},
pages = {5--18},
numpages = {12},
numpages = {14},
isbn = {978-1-4503-6724-0/19/06},
doi = {10.1145/3316482.3326351},
url = {http://doi.acm.org/10.1145/3316482.3326351},
acmid = {3326351},
year = {2019},
month = jun,
location = {Phoenix, AZ, USA},
publisher = {ACM},
address = {New York, NY, USA},
abstract = {Tensor contraction is a fundamental operation in many algorithms with a plethora of applications ranging from quantum chemistry over fluid dynamics and image processing to machine learning. The performance of tensor computations critically depends on the efficient utilization of on-chip memories. In the context of low-power embedded devices, efficient management of the memory space becomes even more crucial, in order to meet energy constraints. This work aims at investigating strategies for performance- and energy-efficient tensor contractions on embedded systems, using racetrack memory (RTM)-based scratch-pad memory (SPM). Compiler optimizations such as the loop access order and data layout transformations paired with architectural optimizations such as prefetching and preshifting are employed to reduce the shifting overhead in RTMs. Experimental results demonstrate that the proposed optimizations improve the SPM performance and energy consumption by 24% and 74% respectively compared to an iso-capacity SRAM.},
acmid = {3326351},
}Downloads
1906_Khan_LCTES [PDF]
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- Asif Ali Khan, Fazal Hameed, Robin Bläsing, Stuart Parkin, Jeronimo Castrillon, "RTSim: A Cycle-accurate Simulator for Racetrack Memories", In IEEE Computer Architecture Letters, IEEE, vol. 18, no. 1, pp. 43–46, Jan 2019. [doi] [Bibtex & Downloads]
RTSim: A Cycle-accurate Simulator for Racetrack Memories
×Reference
Asif Ali Khan, Fazal Hameed, Robin Bläsing, Stuart Parkin, Jeronimo Castrillon, "RTSim: A Cycle-accurate Simulator for Racetrack Memories", In IEEE Computer Architecture Letters, IEEE, vol. 18, no. 1, pp. 43–46, Jan 2019. [doi]
Bibtex
@Article{khan_ieeecal19,
author = {Asif Ali Khan and Fazal Hameed and Robin Bl{\"a}sing and Stuart Parkin and Jeronimo Castrillon},
title = {{RTS}im: A Cycle-accurate Simulator for Racetrack Memories},
journal = {IEEE Computer Architecture Letters},
year = {2019},
volume = {18},
number = {1},
pages = {43--46},
month = jan,
doi = {10.1109/LCA.2019.2899306},
issn = {1556-6056},
publisher = {IEEE},
url = {https://ieeexplore.ieee.org/document/8642352}
}Downloads
1902_khan_IEEECAL [PDF]
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2018
- Fazal Hameed, Asif Ali Khan, Jeronimo Castrillon, "Performance and Energy Efficient Design of STT-RAM Last-Level-Cache", In IEEE Transactions on Very Large Scale Integration Systems (TVLSI), vol. 26, no. 6, pp. 1059–1072, Jun 2018. [doi] [Bibtex & Downloads]
Performance and Energy Efficient Design of STT-RAM Last-Level-Cache
×Reference
Fazal Hameed, Asif Ali Khan, Jeronimo Castrillon, "Performance and Energy Efficient Design of STT-RAM Last-Level-Cache", In IEEE Transactions on Very Large Scale Integration Systems (TVLSI), vol. 26, no. 6, pp. 1059–1072, Jun 2018. [doi]
Abstract
Recent research has proposed having a die-stacked last-level cache (LLC) to overcome the memory wall. Lately, spin-transfer-torque random access memory (STT-RAM) caches have received attention, since they provide improved energy efficiency compared with DRAM caches. However, recently proposed STT-RAM cache architectures unnecessarily dissipate energy by fetching unneeded cache lines (CLs) into the row buffer (RB). In this paper, we propose a selective read policy for the STT-RAM which fetches those CLs into the RB that are likely to be reused. In addition, we propose a tags-update policy that reduces the number of STT-RAM writebacks. This reduces the number of reads/writes and thereby decreases the energy consumption. To reduce the latency penalty of our selective read policy, we propose the following performance optimizations: 1) an RB tags-bypass policy that reduces STT-RAM access latency; 2) an LLC data cache that stores the CLs that are likely to be used in the near future; 3) an address organization scheme that simultaneously reduces LLC access latency and miss rate; and 4) a tags-to-column mapping policy that improves access parallelism. For evaluation, we implement our proposed architecture in the Zesto simulator and run different combinations of SPEC2006 benchmarks on an eight-core system. We compare our approach with a recently proposed STT-RAM LLC with subarray parallelism support and show that our synergistic policies reduce the average LLC dynamic energy consumption by 75% and improve the system performance by 6.5%. Compared with the state-of-the-art DRAM LLC with subarray parallelism, our architecture reduces the LLC dynamic energy consumption by 82% and improves system performance by 6.8%.
Bibtex
@Article{hameed_tvlsi18,
author = {Fazal Hameed and Asif Ali Khan and Jeronimo Castrillon},
title = {Performance and Energy Efficient Design of STT-RAM Last-Level-Cache},
journal = {IEEE Transactions on Very Large Scale Integration Systems (TVLSI)},
year = {2018},
volume = {26},
number = {6},
pages = {1059--1072},
month = jun,
abstract = {Recent research has proposed having a die-stacked last-level cache (LLC) to overcome the memory wall. Lately, spin-transfer-torque random access memory (STT-RAM) caches have received attention, since they provide improved energy efficiency compared with DRAM caches. However, recently proposed STT-RAM cache architectures unnecessarily dissipate energy by fetching unneeded cache lines (CLs) into the row buffer (RB). In this paper, we propose a selective read policy for the STT-RAM which fetches those CLs into the RB that are likely to be reused. In addition, we propose a tags-update policy that reduces the number of STT-RAM writebacks. This reduces the number of reads/writes and thereby decreases the energy consumption. To reduce the latency penalty of our selective read policy, we propose the following performance optimizations: 1) an RB tags-bypass policy that reduces STT-RAM access latency; 2) an LLC data cache that stores the CLs that are likely to be used in the near future; 3) an address organization scheme that simultaneously reduces LLC access latency and miss rate; and 4) a tags-to-column mapping policy that improves access parallelism. For evaluation, we implement our proposed architecture in the Zesto simulator and run different combinations of SPEC2006 benchmarks on an eight-core system. We compare our approach with a recently proposed STT-RAM LLC with subarray parallelism support and show that our synergistic policies reduce the average LLC dynamic energy consumption by 75\% and improve the system performance by 6.5\%. Compared with the state-of-the-art DRAM LLC with subarray parallelism, our architecture reduces the LLC dynamic energy consumption by 82\% and improves system performance by 6.8\%.},
doi = {10.1109/TVLSI.2018.2804938},
file = {:/Users/jeronimocastrillon/Documents/Academic/mypapers/1803_Hameed_TVLSI.pdf:PDF},
issn = {1063-8210},
numpages = {14},
url = {http://ieeexplore.ieee.org/document/8307465/}
}Downloads
1803_Hameed_TVLSI [PDF]
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- Asif Ali Khan, Fazal Hameed, Jeronimo Castrillon, "NVMain Extension for Multi-Level Cache Systems", Proceedings of the 10th RAPIDO Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, co-located with 13th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC), ACM, pp. 7:1–7:6, New York, NY, USA, Jan 2018. [doi] [Bibtex & Downloads]
NVMain Extension for Multi-Level Cache Systems
×Reference
Asif Ali Khan, Fazal Hameed, Jeronimo Castrillon, "NVMain Extension for Multi-Level Cache Systems", Proceedings of the 10th RAPIDO Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, co-located with 13th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC), ACM, pp. 7:1–7:6, New York, NY, USA, Jan 2018. [doi]
Bibtex
@InProceedings{khan_rapido18,
author = {Asif Ali Khan and Fazal Hameed and Jeronimo Castrillon},
title = {NVMain Extension for Multi-Level Cache Systems},
booktitle = {Proceedings of the 10th RAPIDO Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, co-located with 13th International Conference on High-Performance and Embedded Architectures and Compilers (HiPEAC)},
series = {RAPIDO '18},
year = {2018},
month = jan,
pages = {7:1--7:6},
articleno = {7},
numpages = {6},
url = {http://doi.acm.org/10.1145/3180665.3180672},
doi = {10.1145/3180665.3180672},
acmid = {3180672},
publisher = {ACM},
address = {New York, NY, USA},
location = {Manchester, United Kingdom},
isbn = {978-1-4503-6417-1},
}Downloads
1801_Khan_RAPIDO [PDF]
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