An Energy-Efficient Method of Supporting Flexible Special Instructions in an Embedded Processor with Compact ISA

作者：She Dongrui^*; He Yifan; Corporaal Henk

来源：ACM Transactions on Architecture and Code Optimization, 2013, 10(3): 15.

DOI：10.1145/2509420.2509426

• Algorithms
• Design
• Reconfigurable architecture
• special instruction
• low power
• code generation

In application-specific processor design, a common approach to improve performance and efficiency is to use special instructions that execute complex operation patterns. However, in a generic embedded processor with compact Instruction Set Architecture (ISA), these special instructions may lead to large overhead such as: (i) more bits are needed to encode the extra opcodes and operands, resulting in wider instructions; (ii) more Register File (RF) ports are required to provide the extra operands to the function units. Such overhead may increase energy consumption considerably. %26lt;br%26gt;In this article, we propose to support flexible operation pair patterns in a processor with a compact 24-bit RISC-like ISA using: (i) a partially reconfigurable decoder that exploits the pattern locality to reduce opcode space requirement; (ii) a software-controlled bypass network to reduce operand encoding bit and RF port requirement. An energy-aware compiler backend is designed for the proposed architecture that performs pattern selection and bypass-aware scheduling to generate energy-efficient codes. Though the proposed design imposes extra constraints on the operation patterns, the experimental results show that for benchmark applications from different domains, the average dynamic instruction count is reduced by over 25%, which is only about 2% less than the architecture without such constraints. The proposed architecture reduces total energy by an average of 15.8% compared to the RISC baseline, while the one without constraints achieves almost no improvement due to its high overhead. When high performance is required, the proposed architecture is able to achieve a speedup of 13.8% with 13.1% energy reduction compared to the baseline by introducing multicycle SFU operations.

• 出版日期2013-9