A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress

Alessio Colucci, Alberto Marchisio, Beatrice Bussolino, Voitech Mrazek, Maurizio Martina, Guido Masera, Muhammad Shafique

Abstract: The Capsule Networks (CapsNets) is an advanced form of Convolutional Neural Network (CNN), capable of learning spatial relations and being invariant to transformations. CapsNets requires complex matrix operations which current accelerators are not optimized for, concerning both <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">training</sub> and <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inference</sub> passes. Current state-of-the-art simulators and design space exploration (DSE) tools for DNN hardware neglect the modeling of training operations, while requiring long exploration times that slow down the complete design flow. These impediments restrict the real-world applications of CapsNets (e.g., autonomous driving and robotics) as well as the further development of DNNs in life-long learning scenarios that require training on low-power embedded devices. Towards this, we present <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XploreDL</sub> , a novel framework to perform fast yet high-fidelity DSE for both inference and training accelerators, supporting both CNNs and CapsNets operations. <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XploreDL</sub> enables a resource-efficient DSE for accelerators, focusing on power, area, and latency, highlighting Pareto-optimal solutions which can be a green-lit to expedite the design flow. <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XploreDL</sub> can reach the same fidelity as ARM's SCALE-sim, while providing 600x speedup and having a 50x lower memory-footprint. Preliminary results with a deep CapsNet model on MNIST for training accelerators show promising Pareto-optimal architectures with up to 0.4 TOPS/squared-mm and 800 fJ/op efficiency. With inference accelerators for AlexNet the Pareto-optimal solutions reach up to 1.8 TOPS/squared-mm and 200 fJ/op efficiency.