Shaolin Xie

MaPU: A novel mathematical computing architecture

As the feature size of the semiconductor process is scaling down to 10nm and below, it is possible to assemble systems with high performance processors that can theoretically provide computational power of up to tens of PLOPS. However, the power consumption of these systems is also rocketing up to tens of millions watts, and the actual performance is only around 60% of the theoretical performance. Today, power efficiency and sustained performance have become the main foci of processor designers. Traditional computing architecture such as superscalar and GPGPU are proven to be power inefficient, and there is a big gap between the actual and peak performance. In this paper, we present the MaPU architecture, a novel architecture which is suitable for data-intensive computing with great power efficiency and sustained computation throughput. To achieve this goal, MaPU attempts to optimize the application from a system perspective, including the hardware, algorithm and corresponding program model. It uses an innovative multi-granularity parallel memory system with intrinsic shuffle ability, cascading pipelines with wide SIMD data paths and a state-machine-based program model. When executing typical signal processing algorithms, a single MaPU core implemented with a 40nm process exhibits a sustained performance of 134 GLOPS while consuming only 2.8 W in power, which increases the actual power efficiency by an order of magnitude comparable with the traditional CPU and GPGPU.

A self-indexed register file for efficient arithmetical computing hardware

This paper presents a novel register file with self-indexed features, targeting the DSP/media algorithm with massive data locality. The self-indexed register file (SIRF) contains 128 high-speed registers, 4 input ports and 4 output ports. It can be accessed with the double circular window mode, or simply with the immediate index mode. SIRF can eliminate write after write (WAW) dependence without register renaming in hardware or redundant register allocation in compilers, and it can also reduce the address computation if the accessing pattern satisfies the circular window mode. The SIRF was implemented in a high performance mathematical processor(MaPU). Two detailed application examples, finite impulse response (FIR) filtering and image interpolating, are demonstrated to show how SIRF can accelerate DSP/media algorithms. Evaluation shows that SIRF can dramatically reduce memory access. A 2.88× speed-up and a 20% overall power reduction are observed with the evaluated algorithms.