Optimizing Parallel Matrix Multiplication for the AMD Versal ACAP to Accelerate Deep Learning Inference

Core Concepts

This work investigates the efficient mapping of parallel general matrix multiplication (GEMM) to the AMD Versal Adaptive Compute Accelerated Platform (ACAP) equipped with multiple Artificial Intelligence Engines (AIEs) to accelerate deep learning inference.

Abstract

The paper presents a customized design of parallel GEMM for the AMD Versal ACAP, a heterogeneous system-on-chip that integrates ARM processors, FPGA, and an array of high-performance vector AIEs. The key contributions are: Memory Mapping: The authors leverage the multi-level memory hierarchy of the Versal ACAP, including the FPGA Ultra/Block RAMs and the local memory of the AIE tiles, to efficiently distribute the matrix operands and exploit data reuse. Architecture-Specific Micro-kernel: To address the demand for low-precision inference in deep learning, the authors propose a micro-kernel design that utilizes the SIMD units in the AIE tiles to perform mixed-precision arithmetic. Parallel Design: The authors introduce a parallel GEMM design that distributes the computation across multiple AIE tiles, analyzing the theoretical performance and conducting experimental profiling to demonstrate the high parallel scalability. The paper first provides an overview of high-performance GEMM algorithms and the architecture of the Versal ACAP. It then delves into the details of the customized GEMM design, including the mapping of matrix operands to the memory hierarchy, the micro-kernel implementation, and the parallelization strategy. Finally, the authors present a comprehensive performance analysis, identifying communication bottlenecks and outlining potential optimization strategies.

Stats

The paper reports the following key performance metrics: For a single AIE tile, the GEMM design achieves 31.5 MACs/cycle. When scaling to 32 AIE tiles, the performance per tile only degrades by 5.7%, demonstrating high parallel scalability. The micro-kernel design is able to overlap the data transfers and arithmetic computations, with the total execution time being equivalent to the cost of the data transfers.

Quotes

"The parallel design is highly scalable, with a parallel efficiency that, in a strong scaling scenario, only degrades by 5% when increasing the number of AIE tiles from 1 to 32." "The implementation is memory-bound on this platform mostly due to the low bandwidth of the FPGA Ultra RAM."

Key Insights Distilled From

Mapping Parallel Matrix Multiplication in GotoBLAS2 to the AMD Versal ACAP for Deep Learning

by Jie ... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.15043.pdf

Deeper Inquiries

How can the memory bandwidth bottleneck of the FPGA Ultra RAM be addressed to further improve the performance of the GEMM design

To address the memory bandwidth bottleneck of the FPGA Ultra RAM and further enhance the performance of the GEMM design, several strategies can be implemented: Data Reuse Optimization: Implementing more efficient data reuse techniques within the algorithm can reduce the frequency of data transfers from the FPGA Ultra RAM, thereby alleviating the memory bandwidth constraints. Data Prefetching: Introducing data prefetching mechanisms can help anticipate the data needed for computation and bring it closer to the processing units in advance, reducing the latency associated with fetching data from the Ultra RAM. Memory Hierarchy Optimization: By strategically distributing the data across different levels of the memory hierarchy, such as utilizing the FPGA Block RAM more effectively for certain data blocks, the overall memory bandwidth utilization can be optimized. Parallel Data Loading: Exploiting parallel data loading techniques, where multiple data streams are fetched simultaneously from the Ultra RAM to different processing units, can help increase the overall memory throughput. Hardware Acceleration: Implementing hardware accelerators or custom memory controllers tailored to the specific requirements of the GEMM algorithm can significantly enhance memory access efficiency and alleviate the bottleneck. By implementing a combination of these strategies, the memory bandwidth bottleneck of the FPGA Ultra RAM can be effectively addressed, leading to improved performance and scalability of the GEMM design on the Versal ACAP platform.

What other deep learning workloads, beyond GEMM, can benefit from the customized design and parallelization approach presented in this work

The customized design and parallelization approach presented in this work can benefit various deep learning workloads beyond GEMM, including: Convolutional Neural Networks (CNNs): CNNs are widely used in image recognition and computer vision tasks. The parallelization techniques can be applied to optimize the convolutional layers of CNNs, enhancing their performance on the Versal ACAP. Recurrent Neural Networks (RNNs): RNNs are commonly used in natural language processing and sequential data analysis. The parallel design can be adapted to accelerate the matrix operations in RNNs, improving their inference speed. Transformer Networks: Transformers are pivotal in tasks like language translation and text generation. The parallelization strategy can be leveraged to optimize the self-attention and feedforward layers of transformer models, enhancing their efficiency on the Versal ACAP. Graph Neural Networks (GNNs): GNNs are utilized in graph-based tasks like social network analysis and recommendation systems. The customized design can be tailored to accelerate the graph convolutions in GNNs, improving their performance on the platform. By applying the parallelization and optimization techniques to these deep learning workloads, significant performance gains and efficiency improvements can be achieved on the Versal ACAP.

Given the heterogeneous nature of the Versal ACAP, how can the authors explore the integration of the ARM processors and the FPGA fabric to create a more holistic acceleration solution for deep learning inference

To explore the integration of the ARM processors and the FPGA fabric in the Versal ACAP for a more holistic acceleration solution for deep learning inference, the authors can consider the following approaches: Heterogeneous Computing: Utilize the ARM processors for task orchestration, high-level control, and interfacing with external systems, while leveraging the FPGA fabric for low-level, high-performance computation tasks specific to deep learning inference. Task Offloading: Offload specific compute-intensive tasks from the ARM processors to the FPGA fabric, allowing for parallel execution and acceleration of critical deep learning operations. Custom Accelerator Development: Design custom accelerators within the FPGA fabric tailored to deep learning inference tasks, optimizing performance and energy efficiency for specific neural network architectures. Memory Management: Implement efficient memory management schemes that enable seamless data transfer and sharing between the ARM processors and FPGA fabric, ensuring minimal latency and maximum throughput for deep learning workloads. Dynamic Resource Allocation: Develop algorithms for dynamic resource allocation between the ARM processors and FPGA fabric based on workload demands, ensuring optimal utilization of the heterogeneous resources for deep learning inference tasks. By integrating the ARM processors and FPGA fabric in a synergistic manner, the authors can create a comprehensive acceleration solution that maximizes the capabilities of the Versal ACAP for deep learning inference applications.