Flexible spiking neural network chip speeds up and saves energy in vision tasks
FlexSpIM: An Event-Based Digital Compute-In-Memory Accelerator with Flexible Operand Resolution and Layer-Wise Hybrid Stationarity
Hardware Architecture
Summary
Spiking neural networks (SNNs) are a type of artificial brain model that can quickly and efficiently process information for things like vision. The authors created FlexSpIM, a special chip that performs calculations right where data is stored, allowing it to work faster and use less energy. This chip can change how precise its calculations are and how it handles data during processing, making it adaptable to different tasks. Tests showed that FlexSpIM is more efficient and faster than earlier similar chips, maintaining high accuracy on gesture recognition.
What this means in practice
- •For edge ai developers: Design low-latency, energy-efficient vision systems using adaptable digital CIM chips for spiking neural networks.
- •For wearable device engineers: Incorporate flexible compute-in-memory accelerators to improve battery life and response speed in gesture recognition wearables.
Authors
Nicolas Chauvaux, Adrian Kneip, Charlotte Frenkel
Abstract
Compute-in-memory (CIM) accelerators for spiking neural networks (SNNs) offer a promising solution for achieving $μ$s-level inference latency and ultra-low energy in edge vision applications. However, their limited flexibility at both circuit and system levels restricts their deployment across diverse workloads. This work introduces FlexSpIM, a digital CIM architecture supporting arbitrary operand resolution and shape within a unified storage for weights and neuron states (i.e., membrane potentials). These circuit-level capabilities enable a layer-level hybrid weight- and output-stationary dataflow, maximizing operand reuse and reducing costly on- and off-chip data movement during SNN execution. Measurement results from a fabricated FlexSpIM prototype in 40-nm CMOS demonstrate competitive 1-bit-normalized energy efficiency and higher throughput compared with prior fixed-precision digital CIM-based SNN accelerators, while providing bitwise resolution reconfiguration. Evaluated on the IBM DVS gesture dataset, FlexSpIM achieves 95.8% accuracy while enabling up to 45% energy and 52% latency reductions in large-scale systems compared with fixed stationarity approaches.