论文标题
山核桃：产品定量的内容可寻址内存网络
PECAN: A Product-Quantized Content Addressable Memory Network
论文作者
论文摘要
提出了一种新型的深神经网络（DNN）结构，其中仅通过产品量化（PQ）实现过滤和线性变换。这导致通过内容可寻址内存（CAM）进行自然实现，该内存超越了常规的DNN层操作，仅需要简单的表格查找。为端到端PQ原型训练开发了两个方案，即通过基于角度和距离的相似性，它们的乘法性和加性性质有所不同，具有不同的复杂性 - 准确性折衷。更重要的是，基于距离的方案构成了真正的无乘数DNN解决方案。实验证实了这种具有产品定量内容可寻址内存网络（PECAN）的可行性，该内容对硬件有效的部署具有很大的影响，尤其是在内存计算中。
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.
