Highly-Adaptive Mixed-Precision MAC Unit for Smart and Low-Power Edge Computing

Guillaume Devic, Maxime France-Pillois, Jérémie Salles, Gilles Sassatelli, Abdoulaye Gamatié

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Machine learning algorithms are compute- and memory-intensive. Their execution at the edge on resource-constrained embedded systems is challenging. Data quantization, i.e. data bit-width reduction, contributes to reducing de-facto the memory bandwidth requirement. In order to best exploit this bit-width reduction, a prevailing approach consists of tailored hardware accelerators. Another approach relies on general-purpose compute units with Single Instruction Multiple Data (SIMD) support for reduced data bit-width precision, as in ARM Cortex-M [1] or RISC-V based RI5CY [2] processors. However, such processors only handle a few predefined bit-width ranges, e.g. 8-bit and 16-bit only for the ARM SIMD.This paper proposes a flexible architecture of Multiply-and-Accumulate (MAC) unit allowing asymmetric multiplication for operand sizes in powers of 2, up to 32 bits. The synthesis of this architecture in 28nm FD-SOI technology shows 10% and 25% reduction in area and dynamic power respectively, compared to the RI5CY MAC unit. From the energy-efficiency point of view, up to 50% improvements are achieved.
Original languageEnglish
Title of host publication2021 19th IEEE International New Circuits and Systems Conference (NEWCAS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)978-1-6654-2429-5
ISBN (Print)978-1-6654-2430-1
Publication statusPublished - 25 Jun 2021
Event19th IEEE Interregional New Circuits and Systems Conference, 2021 - Online
Duration: 13 Jun 202116 Jun 2021
Conference number: 19


Conference19th IEEE Interregional New Circuits and Systems Conference, 2021
Abbreviated titleNEWCAS 2021
Internet address

Keywords / Materials (for Non-textual outputs)

  • Multiply-and-Accumulate units
  • MAC
  • Machine Learning
  • Edge-Computing
  • Quantization


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