PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration

arXiv:2305.18334v2 Announce Type: replace-cross
Abstract: Conventional multiply-accumulate (MAC) operations have long dominated computation time for deep neural networks (DNNs), espcially convolutional neural networks (CNNs). Recently, product quantization (PQ) has been applied to these workloads, replacing MACs with memory lookups to pre-computed dot products. To better understand the efficiency tradeoffs of product-quantized DNNs (PQ-DNNs), we create a custom hardware accelerator to parallelize and accelerate nearest-neighbor search and dot-product lookups. Additionally, we perform an empirical study to investigate the efficiency--accuracy tradeoffs of …

abstract arxiv cnns computation convolutional neural networks cs.ar cs.lg dnn efficiency hardware mac memory networks neural networks operations product products quantization type workloads