Improving efficiency in neural network accelerator using operands hamming distance optimization


Neural network accelerator is a key enabler for the on-device AI inference, for which energy efficiency is an important metric. The datapath energy, including the computation energy and the data movement energy among the arithmetic units, claims a significant part of the total accelerator energy. By revisiting the basic physics of the arithmetic logic circuits, we show that the datapath energy is highly correlated with the bit flips when streaming the input operands into the arithmetic units, defined as the hamming distance of the input operand matrices. Based on the insight, we propose a post-training optimization algorithm and a hamming-distance-aware training algorithm to co-design and co-optimize the accelerator and the network synergistically. The experimental results based on post-layout simulation with MobileNetV2 demonstrate on average 2.85× datapath energy reduction and up to 8.51× datapath energy reduction for certain layers.

In Asia and South Pacific Design Automation Conference (ASP-DAC)
Meng Li
Meng Li
Staff Research Scientist

I am currently a staff research scientist and tech lead in the Meta On-Device AI team with a focus on researching and productizing efficient AI algorithms and hardwares for next generation AR/VR devices. I received my Ph.D. degree in the Department of Electrical and Computer Engineering, University of Texas at Austin under the supervision of Prof. David Z. Pan and my bachelor degree in Peking University under the supervision of Prof. Ru Huang and Prof. Runsheng Wang. My research interests include efficient and secure AI algorithms and systems.

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