Energy and Precision Evaluation of a Systolic Array Accelerator Using a Quantization Approach for Edge Computing
Alejandra Sanchez-Flores,
Jordi Fornt,
Lluc Alvarez
et al.
Abstract: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 enhanc… Show more
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