  Recent trends have shown that autonomous agents, such as Autonomous Ground
Vehicles (AGVs), Unmanned Aerial Vehicles (UAVs), and mobile robots,
effectively improve human productivity in solving diverse tasks. However, since
these agents are typically powered by portable batteries, they require
extremely low power/energy consumption to operate in a long lifespan. To solve
this challenge, neuromorphic computing has emerged as a promising solution,
where bio-inspired Spiking Neural Networks (SNNs) use spikes from event-based
cameras or data conversion pre-processing to perform sparse computations
efficiently. However, the studies of SNN deployments for autonomous agents are
still at an early stage. Hence, the optimization stages for enabling efficient
embodied SNN deployments for autonomous agents have not been defined
systematically. Toward this, we propose a novel framework called SNN4Agents
that consists of a set of optimization techniques for designing
energy-efficient embodied SNNs targeting autonomous agent applications. Our
SNN4Agents employs weight quantization, timestep reduction, and attention
window reduction to jointly improve the energy efficiency, reduce the memory
footprint, optimize the processing latency, while maintaining high accuracy. In
the evaluation, we investigate use cases of event-based car recognition, and
explore the trade-offs among accuracy, latency, memory, and energy consumption.
The experimental results show that our proposed framework can maintain high
accuracy (i.e., 84.12% accuracy) with 68.75% memory saving, 3.58x speed-up, and
4.03x energy efficiency improvement as compared to the state-of-the-art work
for NCARS dataset. In this manner, our SNN4Agents framework paves the way
toward enabling energy-efficient embodied SNN deployments for autonomous
agents.
