A framework for energy-efficient neuromorphic computing on edge AI targets

A 2026 paper proposes a hardware-aware optimization framework for deploying adaptive spiking neural networks on power-constrained edge processors — with direct implications for embedded seeker design.

Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization (arxiv 2602.02439) proposes a systematic approach to deploying neuromorphic inference on embedded hardware without sacrificing accuracy. Where most neuromorphic papers demonstrate results on unconstrained workstations or dedicated chips, this one grapples with the reality of deployment on commercial edge processors — FPGAs, small SoCs, and microcontroller-class devices.

The optimization target

The paper frames the problem as a co-design task: the network architecture and the hardware platform are not selected independently. A network optimized without knowledge of its deployment target may be theoretically efficient but practically undeployable. Their framework searches the joint space of network topology (number of layers, neuron counts, connectivity) and hardware configuration (clock speed, memory bandwidth, parallelism) to find implementations that meet a specified accuracy threshold at minimum energy.

The central finding is that naively porting a trained SNN to edge hardware wastes energy by a factor of 3–10x compared to hardware-aware designs. Most of this waste comes from memory accesses: the dominant energy cost in embedded inference is not computation but data movement, and networks designed without awareness of cache sizes and memory hierarchies generate far more off-chip memory traffic than necessary.

Adaptive sparsification

The framework introduces a technique the authors call adaptive sparsification: the network dynamically adjusts its spike rate based on input complexity. Simple inputs — a clear sky with a single moving target — generate sparse spike activity. Complex inputs — a cluttered urban background with multiple moving objects — generate denser activity. The processor resources consumed track the actual task difficulty rather than the worst case.

For a seeker application this is attractive: the hardest part of the detection problem, the terminal phase when the target fills more of the field of view, should require more computation than the initial acquisition at range. An adaptive network naturally allocates more resource where it is needed.

Benchmarks on commercial hardware

The paper evaluates its framework on several commercial edge devices, including an Xilinx Zynq SoC family member and a low-power RISC-V target. Results show that their hardware-aware trained networks achieve target accuracy at 40–60% of the energy consumed by naively ported networks on the same hardware.

The accuracy benchmarks are on standard event-camera datasets — N-MNIST and N-Caltech101, which are classification tasks. The more relevant benchmark, multi-target detection and tracking of fast aerial objects, is not yet in the literature at this hardware class.

What this represents for the seeker problem

The terminal seeker problem is a particularly demanding case: the processing chain needs to run at sensor rate (potentially megaevents per second), produce centroid estimates with sub-millisecond update intervals, and do it in a power envelope of a few watts on a vibrating, thermally stressed airframe.

The energy-efficient framework described in this paper is a step toward making that tractable. It does not solve the problem, but it provides a systematic method for asking “what hardware do we need, and what network architecture fits on it?”

Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimization. arxiv 2602.02439