The relentless advancement of artificial intelligence (AI) and machine
learning (ML) applications necessitates the development of specialized hardware
accelerators capable of handling the increasing complexity and computational
demands. Traditional computing architectures, based on the von Neumann model,
are being outstripped by the requirements of contemporary AI/ML algorithms,
leading to a surge in the creation of accelerators like the Graphcore
Intelligence Processing Unit (IPU), Sambanova Reconfigurable Dataflow Unit
(RDU), and enhanced GPU platforms. These hardware accelerators are
characterized by their innovative data-flow architectures and other design
optimizations that promise to deliver superior performance and energy
efficiency for AI/ML tasks.
This research provides a preliminary evaluation and comparison of these
commercial AI/ML accelerators, delving into their hardware and software design
features to discern their strengths and unique capabilities. By conducting a
series of benchmark evaluations on common DNN operators and other AI/ML
workloads, we aim to illuminate the advantages of data-flow architectures over
conventional processor designs and offer insights into the performance
trade-offs of each platform. The findings from our study will serve as a
valuable reference for the design and performance expectations of research
prototypes, thereby facilitating the development of next-generation hardware
accelerators tailored for the ever-evolving landscape of AI/ML applications.
Through this analysis, we aspire to contribute to the broader understanding of
current accelerator technologies and to provide guidance for future innovations
in the field.

本研究对商业 AI / ML 加速器进行了初步评估和比较，通过对常见 DNN 运算符和其他 AI / ML 工作负载的一系列基准评估，揭示了数据流架构相对传统处理器设计的优势和性能权衡，并为研究原型的设计和性能期望提供了有价值的参考，从而促进为不断发展的 AI / ML 应用领域量身定制的下一代硬件加速器的发展。