Leveraging emerging nonvolatile memory in high-level synthesis with loop transformations

Abstract

To mitigate the “Power Wall” challenges for both mobile devices and data centers, accelerator-rich architecture with normally-off mode has been intensively studied recently. Power/energy optimization in high-level synthesis for accelerator design is critical for such accelerator-rich architecture. The emerging nonvolatile memory (NVM), offers many benefits such as ultra-low leakage power, high density, and instant power-on/off, and therefore is a promising alternative for the hardware accelerator design to achieve further power reduction. However, such NVM suffers from large write energy and latency, which brings new challenges for the buffer allocation in the custom accelerator design. This paper presents the first framework that optimizes NVM allocation in high-level synthesis for custom accelerator design, considering loop transformations. It solves the loop transformation, buffer allocation, and buffer type selection to minimize the memory power consumption, while under area, bandwidth, and performance constraints. This paper formulates the optimization problem, and solves it with a problem-specific designed stimulated annealing solution. Experiments demonstrate 32% extra power reduction compared with the previous method without optimizing loop transformations.