An Energy Efficient Soft SIMD Microarchitecture and Its Application on Quantized CNNs
Pengbo Yu,
Flavio Ponzina,
Alexandre Levisse
et al.
Abstract:The ever-increasing computational complexity and energy consumption of today's applications, such as Machine Learning (ML) algorithms, not only strain the capabilities of the underlying hardware but also significantly restrict their wide deployment at the edge. Addressing these challenges, novel architecture solutions are required by leveraging opportunities exposed by algorithms, e.g., robustness to small-bitwidth operand quantization and high intrinsic data-level parallelism. However, traditional Hardware Si… Show more
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