doi: 10.3850/978-3-9815370-4-8_0414

ABSTRACT

As the energy problem has become a big concern in digital system design, one promising solution is combining the core processor with a multi-purpose accelerator targeting high performance applications. Many modern applications can be approximated by multi-layer perceptron (MLP) models, with little quality loss. However, many current MLP accelerators have several drawbacks, such as the unbalance of their performance and flexibility. In this paper, we propose a scheduling framework to guide mapping MLPs onto limited hardware resources with high performance. The framework successfully solves the main constraints of hardware neural acceleration. Furthermore, we implement a reconfigurable neural architecture (RNA) based on this framework, whose computing pattern can be reconfigured for different MLP topologies. The RNA achieves comparable performance with application-specific accelerators and greater flexibility than other hardware MLPs.

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