PECAN: A Product-Quantized Content Addressable Memory Network

• URL: http://arxiv.org/abs/2208.13571v1
• Date: Sat, 13 Aug 2022 08:33:56 GMT
• Title: PECAN: A Product-Quantized Content Addressable Memory Network
• Authors: Jie Ran, Rui Lin, Jason Chun Lok Li, Jiajun Zhou, Ngai Wong
• Abstract summary: The filtering and linear transform are realized solely with product quantization (PQ) This results in a natural implementation via content addressable memory (CAM) Experiments confirm the feasibility of such Product-Quantized Content Addressable Memory Network (PECAN)
• Score: 6.530758154165138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A novel deep neural network (DNN) architecture is proposed wherein the filtering and linear transform are realized solely with product quantization (PQ). This results in a natural implementation via content addressable memory (CAM), which transcends regular DNN layer operations and requires only simple table lookup. Two schemes are developed for the end-to-end PQ prototype training, namely, through angle- and distance-based similarities, which differ in their multiplicative and additive natures with different complexity-accuracy tradeoffs. Even more, the distance-based scheme constitutes a truly multiplier-free DNN solution. Experiments confirm the feasibility of such Product-Quantized Content Addressable Memory Network (PECAN), which has strong implication on hardware-efficient deployments especially for in-memory computing.

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