State-of-the-Art (SotA) hardware implementations of Deep Neural Networks (DNNs) incur high latencies and costs. Binary Neural Networks (BNNs) are potential alternative solutions to realize faster implementations without losing accuracy. In this paper, we first present a new data mapping, called TacitMap, suited for BNNs implemented based on a Computation-In-Memory (CIM) architecture. TacitMap maximizes the use of available parallelism, while CIM architecture eliminates the data movement overhead. We then propose a hardware accelerator based on optical phase change memory (oPCM) called EinsteinBarrier. Ein-steinBarrier incorporates TacitMap and adds an extra dimension for parallelism through wavelength division multiplexing, leading to extra latency reduction. The simulation results show that, compared to the SotA CIM baseline, TacitMap and EinsteinBarrier significantly improve execution time by up to ~154x and ~3113x, respectively, while also maintaining the energy consumption within 60% of that in the CIM baseline.

翻译：深度神经网络（DNNs）的最先进（SotA）硬件实现方案存在高延迟和高成本问题。二值神经网络（BNNs）作为潜在替代方案，可在保持精度的同时实现更快速的运算。本文首先提出一种适配于基于存内计算（CIM）架构的BNN的新型数据映射方法TacitMap，该方法可最大化利用可用并行性，同时CIM架构消除了数据移动开销。随后我们提出基于光相变存储器（oPCM）的硬件加速器EinsteinBarrier，该加速器融合了TacitMap方法并通过波分复用技术增加并行维度，从而进一步降低延迟。仿真结果表明，与SotA CIM基线相比，TacitMap和EinsteinBarrier分别将执行时间显著提升约154倍和3113倍，同时能耗维持在CIM基线的60%以内。