# MENAGE: Mixed-Signal Event-Driven Neuromorphic Accelerator for Edge Applications

arXiv: https://arxiv.org/abs/2410.08403 · HTML: https://mlsanity.com/paper/2410.08403/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [TimingLLM: A Two-Stage Retrieval-Augmented Framework for Pre-Synthesis Timing Prediction from Verilog](https://mlsanity.com/paper/2604.23602/) (2026)

## Abstract (arXiv)

This paper presents a mixed-signal neuromorphic accelerator architecture designed for accelerating inference with event-based neural network models. This fully CMOS-compatible accelerator utilizes analog computing to emulate synapse and neuron operations. A C2C ladder structure implements synapses, while operational amplifiers (op-amps) are used to realize neuron functions. To enhance hardware resource utilization and power efficiency, we introduce the concept of a virtual neuron, where a single neuron engine emulates a set of model neurons, leveraging the sparsity inherent in event-based neuromorphic systems. Additionally, we propose a memory-based control technique to manage events in each layer, which improves performance while maintaining the flexibility to support various layer types. We also introduce an integer linear programming (ILP)-based mapping approach for efficiently allocating the model onto the proposed accelerator. The accelerator is a general-purpose neuromorphic platform capable of executing linear and convolutional neural models. The effectiveness of the proposed architecture is evaluated using two specially designed neuromorphic accelerators and two event-based datasets. The results show that the proposed architecture achieves 12.1 TOPS/W energy efficiency when accelerating a model trained on CIFAR10-DVS.