Run-time Reconfigurable Approximate Architecture

Approximate Computing or Inexact Computing can be seen as one solution for building highly energy-efficient systems. With continuous technology scaling, roadblocks on reducing supply voltage, and escalating power density and thermal issues, traditional low power techniques like dynamic voltage and frequency scaling (DVFS) and power-gating no longer suffice to provide a significant reduction in total chip power consumption. With approximate computing, accuracy and precision bounds of computation can be relaxed, enabling significant improvements in the area, power, and performance (e.g. circuit delay) of on-chip systems while keeping output quality within an acceptable range.

The relevant ongoing works in the area of approximate computing (AC) consider both the hardware and software level. A major limitation of these works is that hardware-wise, only fine-grained elements, and only a few specific circuit techniques are considered, rather than complex embedded architectures. Furthermore, the existing techniques are restricted or at least best-suited to specific applications whose behavior is well understood statically. In more general contexts where the accuracy requirement can change dynamically, such static techniques need to be extended to dynamic methods. In this project, we intend to provide a systematic approach to support run-time accuracy reconfigurable designs in hardware. We intend to improve energy efficiency as much as possible where the runtime system  (RTS) will use the accuracy requirements of the application and reconfigure the architectural components with the desired level of inaccuracy. Our approach is orthogonal to any software optimizations. Even for optimized algorithms, we expect substantial savings by reconfiguring the hardware to the precisely needed accuracy.

Project title: Run-time Reconfigurable Approximate Architecture

Acronym: ReAp

Research Grant: Funded by Deutsche Forschungsgemeinschaft (DFG). Amount: 191,900 Euros

Project Duration:  February 2018 -- August 2020

Team

• Prof. Dr. Akash Kumar (Head of Supervision)
• Salim Ullah (Research Associate)
• Zahra Ebrahimi (Research Associate)