一种用于高效近传感器数据分析的跨精度浮点计算集群
A transprecision floating-point cluster for efficient near-sensor data analytics
AI总结：
针对近传感器计算对浮点运算的需求，提出基于RISC-V的跨精度浮点多核集群，结合并行化、子字向量化与近阈值运行，经FPGA探索与基准测试验证，能效优于现有前沿方案。
摘要：
近传感器计算领域的最新应用需要采用浮点运算，以兼顾高精度结果与宽动态范围。本文提出了一款多核计算集群，它利用跨精度计算的细粒度可调原理，在最低功耗预算下为近传感器应用提供支持。我们的设计基于开源RISC-V架构，将并行化、子字向量化与近阈值运行相结合，打造出一个高度可扩展、功能多样的系统。我们在周期精确的FPGA仿真器上对跨精度集群的设计空间进行了详尽探索，旨在找出在性能、能效和面积效率方面最优的配置。我们还提供了完善的软件栈支持，包括并行运行时和编译工具链，以支持端到端应用的开发。我们在一组具有近传感器处理领域代表性的基准测试上对设计进行了实验评估，并通过布局布线后的功耗分析对时序结果进行了补充。最后，与现有前沿技术的对比表明，我们的方案在能效上优于同类方案，单精度标量下峰值能效达97 Gflop/s/W，半精度向量下达162 Gflop/s/W。
英文摘要：
Recent applications in the domain of near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this paper, we propose a multi-core computing cluster that leverages the fined-grained tunable principles of transprecision computing to provide support to near-sensor applications at a minimum power budget. Our design - based on the open-source RISC-V architecture - combines parallelization and sub-word vectorization with near-threshold operation, leading to a highly scalable and versatile system. We perform an exhaustive exploration of the design space of the transprecision cluster on a cycle-accurate FPGA emulator, with the aim to identify the most efficient configurations in terms of performance, energy efficiency, and area efficiency. We also provide a full-fledged software stack support, including a parallel runtime and a compilation toolchain, to enable the development of end-to-end applications. We perform an experimental assessment of our design on a set of benchmarks representative of the near-sensor processing domain, complementing the timing results with a post place-&-route analysis of the power consumption. Finally, a comparison with the state-of-the-art shows that our solution outperforms the competitors in energy efficiency, reaching a peak of 97 Gflop/s/W on single-precision scalars and 162 Gflop/s/W on half-precision vectors.
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