NeuroFlex: Column-Exact ANN-SNN Co-Execution Accelerator with Cost-Guided Scheduling

TL;DR

NeuroFlex is a column-level accelerator that co-executes artificial and spiking neural networks to reduce energy-delay product on sparse edge workloads, achieving significant improvements in throughput and energy efficiency.

Contribution

It introduces a novel column-exact hybrid execution method with cost-guided scheduling for ANN-SNN co-processing, enhancing performance and energy efficiency.

Findings

Improves throughput by 16-19% over random mapping.

Reduces energy-delay product by 57-67% compared to ANN baseline.

Delivers up to 2.5x speedup and 2.51x energy reduction.

Abstract

NeuroFlex is a column-level accelerator that co-executes artificial and spiking neural networks to minimize energy-delay product on sparse edge workloads with competitive accuracy. The design extends integer-exact QCFS ANN-SNN conversion from layers to independent columns. It unifies INT8 storage with on-the-fly spike generation using an offline cost model to assign columns to ANN or SNN cores and pack work across processing elements with deterministic runtime. Our cost-guided scheduling algorithm improves throughput by 16-19% over random mapping and lowers EDP by 57-67% versus a strong ANN-only baseline across VGG-16, ResNet-34, GoogLeNet, and BERT models. NeuroFlex also delivers up to 2.5x speedup over LoAS and 2.51x energy reduction over SparTen. These results indicate that fine-grained and integer-exact hybridization outperforms single-mode designs on energy and latency without sacrificing accuracy.