ASTER: Attention-based Spiking Transformer Engine for Event-driven Reasoning

TL;DR

This paper introduces a specialized hardware accelerator for spiking transformer models, combining analog-digital PIM architecture and software optimizations to enable efficient, low-power, real-time event-driven visual reasoning on edge devices.

Contribution

The work presents a novel memory-centric hybrid analog-digital PIM accelerator and inference optimizations tailored for spiking transformers, addressing hardware and algorithmic challenges.

Findings

Achieves up to 467x energy reduction over edge GPU

Maintains competitive accuracy on ImageNet

Enables low-power real-time event-driven visual processing

Abstract

The integration of spiking neural networks (SNNs) with transformer-based architectures has opened new opportunities for bio-inspired low-power, event-driven visual reasoning on edge devices. However, the high temporal resolution and binary nature of spike-driven computation introduce architectural mismatches with conventional digital hardware (CPU/GPU). Prior neuromorphic and Processing-in-Memory (PIM) accelerators struggle with high sparsity and complex operations prevalent in such models. To address these challenges, we propose a memory-centric hardware accelerator tailored for spiking transformers, optimized for deployment in real-time event-driven frameworks such as classification with both static and event-based input frames. Our design leverages a hybrid analog-digital PIM architecture with input sparsity optimizations, and a custom-designed dataflow to minimize memory access overhead and maximize data reuse under spatiotemporal sparsity, for compute and memory-efficient end-to-end execution of spiking transformers. We subsequently propose inference-time software optimizations for layer skipping, and timestep reduction, leveraging Bayesian Optimization with surrogate modeling to perform robust, efficient co-exploration of the joint algorithmic-microarchitectural design spaces under tight computational budgets. Evaluated on both image(ImageNet) and event-based (CIFAR-10 DVS, DVSGesture) classification, the accelerator achieves up to ~467x and ~1.86x energy reduction compared to edge GPU (Jetson Orin Nano) and previous PIM accelerators for spiking transformers, while maintaining competitive task accuracy on ImageNet dataset. This work enables a new class of intelligent ubiquitous edge AI, built using spiking transformer acceleration for low-power, real-time visual processing at the extreme edge.