Peephole optimization of asynchronous macromodule networks

Journal ArticleMost high level synthesis tools for asynchronous circuits take descriptions in concurrent hardware description languages and generate networks of macromodules or handshake components. In this paper we describe a peephole optimizer for such macromodule networks that often effects area and/or time improvements. Our optimizer first deduces an equivalent black-box behavior for the given network of macrmodules using Dill's trace-theoretic parallel composition operator. It then applies a new procedure culled Burst-mode reduction to obtain burst-mode machines, which can be synthesized into gate networks using available tools. Since burst-mode reduction can be applied to any macromodule network that is delay-insensitive as well as deterministic, our optimizer covers a significant number of asynchronous circuits especially those generated by asynchronous high level synthesis tools

Peephole optimization of asynchronous macromodule networks

Journal ArticleAbstract- Most high-level synthesis tools for asynchronous circuits take descriptions in concurrent hardware description languages and generate networks of macromodules or handshake components. In this paper, we propose a peephole optimizer for these networks. Our peephole optimizer first deduces an equivalent blackbox behavior for the network using Dill's tracetheoretic parallel composition operator. It then applies a new procedure called burst-mode reduction to obtain burst-mode machines from the deduced behavior. In a significant number of examples, our optimizer achieves gate-count improvements by a factor of five, and speed (cycle-time) improvements by a factor of two. Burst-mode reduction can be applied to any macromodule network that is delay insensitive as well as deterministic. A significant number of asynchronous circuits, especially those generated by asynchronous high-level synthesis tools, fall into this class, thus making our procedure widely applicable

Microarchitecture optimization for timing and layout

In recent years the drive to produce more complex integrated circuits while spending less design time has driven the demand for design automation tools. The search for design automation methods has resulted in the design of numerous behavioral synthesis and logic synthesis tools. This report describes a system that fills the gap between traditional behavioral synthesis and logic synthesis tools. Techniques are introduced for improving the microarchitecture structure and using feedback from lower-level optimization tools to guide design optimizations while attempting to meet user specified area and time constraints. These techniques include the capability for mixing layout styles such as custom layout for random-logic components and bit-slicing for regularly structured components. In this manner the entire design, control logic and datapath, can be optimized at the same time. Further, this paper presents a new methodology for microarchitecture-level optimization that greatly reduces the amount of technology-specific knowledge necessary to perform the optimizations

Interstellar: Using Halide's Scheduling Language to Analyze DNN Accelerators

We show that DNN accelerator micro-architectures and their program mappings represent specific choices of loop order and hardware parallelism for computing the seven nested loops of DNNs, which enables us to create a formal taxonomy of all existing dense DNN accelerators. Surprisingly, the loop transformations needed to create these hardware variants can be precisely and concisely represented by Halide's scheduling language. By modifying the Halide compiler to generate hardware, we create a system that can fairly compare these prior accelerators. As long as proper loop blocking schemes are used, and the hardware can support mapping replicated loops, many different hardware dataflows yield similar energy efficiency with good performance. This is because the loop blocking can ensure that most data references stay on-chip with good locality and the processing units have high resource utilization. How resources are allocated, especially in the memory system, has a large impact on energy and performance. By optimizing hardware resource allocation while keeping throughput constant, we achieve up to 4.2X energy improvement for Convolutional Neural Networks (CNNs), 1.6X and 1.8X improvement for Long Short-Term Memories (LSTMs) and multi-layer perceptrons (MLPs), respectively.Comment: Published as a conference paper at ASPLOS 202