Multi-reward optimization using genetic algorithms for edge AI

Due to evolving chip architectures that support artificial intelligence (AI) on the edge, producing models that achieve top performance on small devices with limited resources has become a priority. The challenge is to construct superior deep neural networks by finding viable solutions in the face of memory limitations, computational bottlenecks, latency requirements, and power restrictions. Past research has primarily been focused on improving models by optimizing only a subset of the latency, memory, and power consumption aspects. We propose that possible solutions can be found using genetic algorithms by posing the above variables as part of a multi-reward optimization problem. Further, few methods have incorporated the device itself in the training process efficiently. In this paper, we construct an initial population of viable network layers, and their respective weights as genes for the proposed genetic algorithm. We then track these layers’ viability through generations by constructing neural networks and monitoring an amalgamated score, which we term as influence, representing multiple metrics. To facilitate device-specific optimization, all network layers are constructed while maintaining the requirement that portability to a target device remains possible – failure to meet this requirement results in removal from the population. Furthermore, upon construction, networks are also validated to ensure portability prior to evaluation. The proposed genetic algorithm is utilized as a neural architecture search (NAS) strategy where we optimize constructed networks’ performance in latency, accuracy, and memory on the target device. For this work, the ultra-low-power MAXIM 78002 framework is utilized to define layer and network constraints; results against the CIFAR10 computer vision dataset are presented.