Automatic generation of custom instruction processors from high-level application descriptions enables fast design space exploration, while offering very favorable performance and silicon area combinations. This work introduces a novel method for adapting the instruction set to match an application captured in a high-level language. A simplified model is used to find the optimal instructions via enumeration of maximal convex subgraphs of application data flow graphs (DFGs). Our experiments involving a set of multi-

Abstract-This paper describes an integer-linear-programming (ILP)-based system called Custom Hardware Instruction Processor Synthesis (CHIPS) that identifies custom instructions for critical code segments, given the available data bandwidth and transfer latencies between custom logic and a baseline processor with architecturally visible state registers. Our approach enables designers to optionally constrain the number of input and output operands for custom instructions. We describe a design flow to identify promising area, performance, and code-size tradeoffs. We study the effect of input/output constraints, register-file ports, and compiler transformations such as if-conversion. Our experiments show that, in most cases, the solutions with the highest performance are identified when the input/output constraints are removed. However, input/output constraints help our algorithms identify frequently used code segments, reducing the overall area overhead. Results for 11 benchmarks covering cryptography and multimedia are shown, with speed-ups between 1.7 and 6.6 times, code-size reductions between 6% and 72%, and area costs ranging between 12 and 256 adders for maximum speed-up. Our ILP-based approach scales well: benchmarks with basic blocks consisting of more than 1000 instructions can be optimally solved, most of the time within a few seconds.Index Terms-Application-specific instruction-set processors (ASIPs), custom instructions, customizable processors, extensible processors, integer linear programming (ILP), optimization algorithms.

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