Algorithm/Hardware Co-Design for In-Memory Neural Network Computing with Minimal Peripheral Circuit Overhead

We propose an in-memory neural network accelerator architecture called MOSAIC which uses minimal form of peripheral circuits; 1-bit word line driver to replace DAC and 1-bit sense amplifier to replace ADC. To map multi-bit neural networks on MOSAIC architecture which has 1-bit precision peripheral circuits, we also propose a bit-splitting method to approximate the original network by separating each bit path of the multi-bit network so that each bit path can propagate independently throughout the network. Thanks to the minimal form of peripheral circuits, MOSAIC can achieve an order of magnitude higher energy and area efficiency than previous in-memory neural network accelerators.