TFSRAM: A 249.8TOPS/W Timing-to-first-spike Compute-in-memory Neuromorphic Processing Engine with Twin-column SRAM Synapses

Spiking neural networks (SNNs) have shown high efficiency in information processing. Time-to-first-spike (TTFS) coding, which encodes the information to the times of first spikes, has been introduced in SNNs due to its simple data representation. However, there is a lack of studies in synapse mapping and neuron circuits for compute-in-memory (CIM) neuromorphic design for low-power TTFS-based SNN inference. This work presents a CIM neuromorphic processing engine for TTFS-based SNNs, which comprises a 64x64 8T-SRAM array for synapse storage and current-based post-neuron circuits. We propose to map the positive and negative synaptic weights to adjacent columns in the SRAM array and accumulate the membrane potential in the dedicated post-neuron circuits. Two techniques are presented through hardware-software co-design: ”Multi-Level Firing Threshold Adjustment” to mitigate the impact of process variations, and “Timing Threshold Adjustment” to further speed up the computation. The proposed processing engine achieves the energy efficiency 249.8 TOPS/W under 8-bit inputs and signed 4-bit weights.