CGLA: Coarse-Grained Linear Array for Multi-Hash Acceleration in Blockchain Mining

In emerging blockchain-based IoT systems, highly flexible and energy-efficient hash function hardware design is necessary to maintain the operation of diverse blockchain networks. Accordingly, a coarse-grained reconfigurable array (CGRA) is the most optimal architecture for implementing hash functions; however, current CGRA-based works still have slow speeds and low energy efficiency. To solve these problems, this paper proposes a Coarse-Grained Linear Array (CGLA), upgrading from the CGRA, to perform multiple hash functions with high speed and energy efficiency. To achieve that goal, three main ideas are proposed: a self-updating data method, an expandable processing element array (PEA), and an efficient arithmetic logic unit (ALU) dedicated to hash functions. Our CGLA has been successfully implemented on a TySOM-3A FPGA. Evaluations on 45nm ASICs show that the CGLA is 2.8-8.7 times more power efficient than GPUs, and 1.3-17.8 times and 1.9-44.5 times better in throughput and energy efficiency than previous CGRAs.