학술논문
AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms
Document Type
Conference
Author
Source
2022 Design, Automation & Test in Europe Conference & Exhibition (DATE) Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022. :238-243 Mar, 2022
Subject
Language
ISSN
1558-1101
Abstract
We present AnaCoNGA, an analytical co-design methodology, which enables two genetic algorithms to evaluate the fitness of design decisions on layer-wise quantization of a neural network and hardware (HW) resource allocation. We embed a hardware architecture search (HAS) algorithm into a quantization strategy search (QSS) algorithm to evaluate the hardware design Pareto-front of each considered quantization strategy. We harness the speed and flexibility of analytical HW-modeling to enable parallel HW-CNN co-design. With this approach, the QSS is focused on seeking high-accuracy quantization strategies which are guaranteed to have efficient hardware designs at the end of the search. Through AnaCoNGA, we improve the accuracy by 2.88 p.p. with respect to a uniform 2-bit ResNet20 on CIFAR-10, and achieve a 35% and 37% improvement in latency and DRAM accesses, while reducing LUT and BRAM resources by 9% and 59% respectively, when compared to a standard edge variant of the accelerator. The nested genetic algorithm formulation also reduces the search time by 51% compared to an equivalent, sequential co-design formulation.