The Mozart reuse exposed dataflow processor for AI and beyond: industrial product

Abstract

In this paper we introduce the Mozart Processor, which implements a new processing paradigm called Reuse Exposed Dataflow (RED). RED is a counterpart to existing execution models of Von-Neumann, SIMT, Dataflow, and FPGA. Dataflow and data reuse are the fundamental architecture primitives in RED, implemented with mechanisms for inter-worker communication and synchronization. The paper defines the processor architecture, the details of the microarchitecture, chip implementation, software stack development, and performance results. The architecture's goal is to achieve near-CPU like flexibility while having ASIC-like efficiency for a large-class of data-intensive workloads. An additional goal was software maturity --- have large coverage of applications immediately, avoiding the need for a long-drawn hand-tuning software development phase. The architecture was defined with this software-maturity/compiler friendliness in mind. In short, the goal was to do to GPUs, what GPUs did to CPUs --- i.e. be a better solution for a large range of workloads, while preserving flexibility and programmability. The chip was implemented with HBM and PCIe interfaces and taken to production on a 16nm TSMC FFC process. For ML inference tasks with batch-size=4, Mozart is integer factors better than state-of-the-art GPUs even while being nearly 2 technology nodes behind. We conclude with a set of lessons learned, the unique challenges of a clean-slate architecture in a commercial setting, and pointers for uncovered research problems.