CLAN: Continuous Learning using Asynchronous Neuroevolution on Commodity Edge Devices

TL;DR

This paper presents CLAN, a system enabling continuous learning on edge devices using asynchronous neuroevolution, reducing reliance on cloud infrastructure and improving scalability for AI agents in real-world applications.

Contribution

The paper introduces a distributed neuroevolution approach on commodity edge devices, with novel communication reduction techniques for scalable continuous learning.

Findings

Achieved up to 3.6x reduction in communication during learning

Demonstrated effective collaborative learning on Raspberry Pi clusters

Provided insights for algorithm-hardware co-design for edge AI

Abstract

Recent advancements in machine learning algorithms, especially the development of Deep Neural Networks (DNNs) have transformed the landscape of Artificial Intelligence (AI). With every passing day, deep learning based methods are applied to solve new problems with exceptional results. The portal to the real world is the edge. The true impact of AI can only be fully realized if we can have AI agents continuously interacting with the real world and solving everyday problems. Unfortunately, high compute and memory requirements of DNNs acts a huge barrier towards this vision. Today we circumvent this problem by deploying special purpose inference hardware on the edge while procuring trained models from the cloud. This approach, however, relies on constant interaction with the cloud for transmitting all the data, training on massive GPU clusters, and downloading updated models. This is challenging for bandwidth, privacy, and constant connectivity concerns that autonomous agents may exhibit. In this paper we evaluate techniques for enabling adaptive intelligence on edge devices with zero interaction with any high-end cloud/server. We build a prototype distributed system of Raspberry Pis communicating via WiFi running NeuroEvolutionary (NE) learning and inference. We evaluate the performance of such a collaborative system and detail the compute/communication characteristics of different arrangements of the system that trade-off parallelism versus communication. Using insights from our analysis, we also propose algorithmic modifications to reduce communication by up to 3.6x during the learning phase to enhance scalability even further and match performance of higher end computing devices at scale. We believe that these insights will enable algorithm-hardware co-design efforts for enabling continuous learning on the edge.