A Multi-Cache System for On-Chip Memory Optimization in FPGA-Based CNN Accelerators

Abstract

In recent years, FPGAs have demonstrated remarkable performance and contained power consumption for the on-the-edge inference of Convolutional Neural Networks. One of the main challenges in implementing this class of algorithms on board an FPGA is resource management, especially with regard to memory. This work presents a multi-cache system that allows for noticeably shrinking the required on-chip memory with a negligible variation of timing performance and power consumption. The presented methods have been applied to the CloudScout CNN, which was developed to perform cloud detection directly on board the satellite, thus representing a relevant case study for on the edge applications. The system was validated and characterized on a Xilinx ZCU106 Evaluation Board. The result is a 64.48% memory saving if compared to an alternative hardware accelerator developed for the same algorithm, with comparable performance in terms of inference time and power consumption. The paper also presents a detailed analysis of the hardware accelerator power consumption, focusing on the impact of data transfer between the accelerator and the external memory. Further investigation shows that the proposed strategies allow the implementation of the accelerator on FPGAs with a smaller size, guaranteeing benefits in terms of power consumption and hardware costs. A broader evaluation about the applicability of the presented methods to other models demonstrates valuable results in terms of memory saving with respect to other works reported in the literature.

FPG-AI: A Technology-Independent Framework for the Automation of CNN Deployment on FPGAs

“…Nonetheless, HLS tools focus on the mapping and allocation of low-level primitive operations, exploring a large design space that is partially affected by the specific application domain [34]. On the other hand, Register Transfer Level (RTL) designs are often used to achieve extremely optimized architectures for those applications with a highly structured nature, such as CNN workloads [34]- [36]. A common approach is to merge model compression and architecture development into an end-to-end design flow to take advantage of both these techniques [37].…”