Adaptive Quantization as a Device-Algorithm Co-Design Approach to Improve the Performance of In-Memory Unsupervised Learning With SNNs

Off-chip memory access is the primary bottleneck toward accelerating neural network operations and reducing energy consumption. In-memory training and computation using emerging nonvolatile memories (eNVMs) have been proposed to address this problem. However, a small number of conductance states limit in-memory online learning performance. Here, we introduce a device-algorithm co-design approach and its application to phase change memory (PCM) for improving learning accuracy. We present an adaptive quantization method, which compensates the accuracy loss due to limited conductance levels and enables high-accuracy unsupervised learning with low-precision eNVM devices. We develop a spiking neural network framework for NeuroSim platform to compare online learning performance of PCM arrays for analog and digital implementations and benchmark the tradeoffs in energy consumption, latency, and area.