QuTiBench：异构硬件上的神经网络基准测试
QuTiBench: Benchmarking Neural Networks on Heterogeneous Hardware
摘要：
神经网络已经成为最成功的通用机器学习算法之一。例如，它们在实现机器视觉和语音识别方面发挥着关键作用。它们的计算复杂性是巨大的，并且伴随着同样具有挑战性的存储器要求，这限制了特别是在能量受限的嵌入式环境中的部署。为了解决这些实施挑战，出现了广泛的新的定制和异构硬件架构，通常伴随着共同设计的算法，以从硬件中提取最大的好处。此外，许多优化技术正在探索神经网络，以减少计算和存储器的要求，同时保持准确性。这导致了大量的算法和架构选择，其中一些比其他更适合特定的用例。对于系统级设计人员来说，目前还没有比较各种硬件、算法和优化选项的好方法。虽然在这一领域有许多基准测试工作，但它们只覆盖了嵌入式设计领域的各个部分。现有的基准测试都不支持基本的算法优化，如量化，这是一种留在芯片上的重要技术，也不支持专门的异构硬件架构。我们提出了一个新的基准套件，QuTiBench，满足这一需求。QuTiBench是一种新型的多层基准测试方法，支持量化等算法优化，帮助系统开发人员了解这些新型计算架构在特定神经网络方面的优势和局限性，并将有助于推动未来的创新。我们邀请社区为QuTiBench做出贡献，以支持实施机器学习系统的全方位选择。
英文摘要：
Neural Networks have become one of the most successful universal machine learning algorithms. They play a key role in enabling machine vision and speech recognition for example. Their computational complexity is enormous and comes along with equally challenging memory requirements, which limits deployment in particular within energy constrained, embedded environments. In order to address these implementation challenges, a broad spectrum of new customized and heterogeneous hardware architectures have emerged, often accompanied with co-designed algorithms to extract maximum benefit out of the hardware. Furthermore, numerous optimization techniques are being explored for neural networks to reduce compute and memory requirements while maintaining accuracy. This results in an abundance of algorithmic and architectural choices, some of which fit specific use cases better than others. For system level designers, there is currently no good way to compare the variety of hardware, algorithm and optimization options. While there are many benchmarking efforts in this field, they cover only subsections of the embedded design space. None of the existing benchmarks support essential algorithmic optimizations such as quantization, an important technique to stay on chip, or specialized heterogeneous hardware architectures. We propose a novel benchmark suite, QuTiBench, that addresses this need. QuTiBench is a novel multi-tiered benchmarking methodology that supports algorithmic optimizations such as quantization and helps system developers understand the benefits and limitations of these novel compute architectures in regard to specific neural networks and will help drive future innovation. We invite the community to contribute to QuTiBench in order to support the full spectrum of choices in implementing machine learning systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
