  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.
