Accelerators in Embedded Systems for Machine Learning: A RISCV View

Sanchez-Flores, Alejandra; Alvarez, Lluc; Alorda-Ladaria, Bartomeu

doi:10.1109/dcis58620.2023.10335969

2023 38th Conference on Design of Circuits and Integrated Systems (DCIS) 2023

DOI: 10.1109/dcis58620.2023.10335969

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Accelerators in Embedded Systems for Machine Learning: A RISCV View

Alejandra Sanchez-Flores,

Lluc Alvarez,

Bartomeu Alorda-Ladaria

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2024

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Cited by 1 publication

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Energy and Precision Evaluation of a Systolic Array Accelerator Using a Quantization Approach for Edge Computing

Sanchez-Flores,

Fornt,

Alvarez

et al. 2024

Electronics

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This paper focuses on the implementation of a neural network accelerator optimized for speed and energy efficiency, for use in embedded machine learning. Specifically, we explore power reduction at the hardware level through systolic array and low-precision data systems, including quantized approaches. We present a comprehensive analysis comparing a full precision (FP16) accelerator with a quantized (INT16) version on an FPGA. We upgraded the FP16 modules to handle INT16 values, employing data shifts to enhance value density while maintaining accuracy. Through single convolution experiments, we assess the energy consumption and error minimization. The paper’s structure includes a detailed description of the FP16 accelerator, the transition to quantization, mathematical and implementation insights, instrumentation for power measurement, and a comparative analysis of power consumption and convolution error. Our results attempt to identify a pattern in 16-bit quantization to achieve significant power savings with minimal loss of accuracy.

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Energy and Precision Evaluation of a Systolic Array Accelerator Using a Quantization Approach for Edge Computing

Sanchez-Flores,

Fornt,

Alvarez

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

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Accelerators in Embedded Systems for Machine Learning: A RISCV View

Cited by 1 publication

References 25 publications

Energy and Precision Evaluation of a Systolic Array Accelerator Using a Quantization Approach for Edge Computing

Energy and Precision Evaluation of a Systolic Array Accelerator Using a Quantization Approach for Edge Computing

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