Enhancing GEMM Acceleration on a Loosely-Coupled Multi-core Processor with Tile-based Instruction Set and Predictive Address Translation

Основні поняття

MACO, a novel loosely-coupled multi-core general-purpose architecture, integrates multiple CPU+GEMM Acceleration Engines (MMAEs) and employs a tile-based instruction set and predictive address translation to enhance the flexibility, programmability, and computational efficiency for GEMM-related applications.

Анотація

The paper proposes MACO, a loosely-coupled multi-core general-purpose processor architecture optimized for GEMM-related applications. MACO consists of up to 16 homogeneous compute nodes interconnected by a network-on-chip (NOC), with each node integrating a CPU core and a dedicated MMAE (Matrix Multiplication Acceleration Engine).

To improve the programmability and flexibility of MACO, the authors introduce a tile-based instruction set architecture called MPAIS, which provides instructions for data migration, tile GEMM computation, and task management. MACO also employs techniques such as hardware-assisted data prefetching and locking, as well as predictive address translation, to enhance the computational efficiency for GEMM workloads.

The experimental results demonstrate that MACO exhibits good scalability, achieving an average computational efficiency of 90% across multiple cores. Furthermore, MACO can achieve up to 1.1 TFLOPS with 88% computational efficiency on state-of-the-art deep neural network workloads, indicating its adaptivity to deep learning applications.

Статистика

MACO CPU core: 2.2 GHz, 6.25 mm^2, 2 W, 8 FMACs, 35.2 GFLOPS (FP64) / 71 GFLOPS (FP32)
MACO MMAE: 2.5 GHz, 1.58 mm^2, 1.5 W, 16 FMACs, 80 GFLOPS (FP64) / 160 GFLOPS (FP32) / 320 GFLOPS (FP16)

Цитати

"MACO exhibits good scalability, achieving an average computational efficiency of 90% across multiple cores."
"MACO can achieve up to 1.1 TFLOPS with 88% computational efficiency on state-of-the-art deep neural network workloads, indicating its adaptivity to deep learning applications."

Ключові висновки, отримані з

MACO: Exploring GEMM Acceleration on a Loosely-Coupled Multi-core Processor

by Bingcai Sui,... о arxiv.org 05-01-2024

https://arxiv.org/pdf/2404.19180.pdf

Глибші Запити

How can the tile-based instruction set of MACO be extended or customized to support a wider range of GEMM-related applications beyond deep learning

The tile-based instruction set of MACO can be extended or customized to support a wider range of GEMM-related applications beyond deep learning by incorporating additional specialized instructions tailored to specific application requirements. For instance, the instruction set could be expanded to include operations optimized for scientific computing, financial modeling, image processing, or any other domain that heavily relies on matrix-matrix multiplications. These new instructions could cater to different data types, precision levels, or matrix sizes commonly found in diverse GEMM workloads. By introducing a flexible and extensible instruction set architecture, MACO can adapt to a broader spectrum of applications, enhancing its versatility and utility in various computational tasks.

What are the potential trade-offs or limitations of the predictive address translation technique used in MACO, and how could it be further improved

The predictive address translation technique used in MACO offers significant benefits in reducing memory access overhead and improving computational efficiency. However, there are potential trade-offs and limitations associated with this approach. One limitation is the reliance on accurate predictions, which may introduce overhead if the predictions are incorrect, leading to unnecessary pre-fetching of data. To address this, the predictive model used for address translation could be enhanced by incorporating machine learning algorithms to adaptively adjust prediction strategies based on historical data access patterns. Additionally, the system could implement dynamic feedback mechanisms to validate predictions and adjust caching strategies in real-time, minimizing the impact of inaccurate predictions. By continuously refining the predictive address translation mechanism, MACO can mitigate potential trade-offs and further optimize memory access performance.

Given the heterogeneous nature of MACO, how could the parallel execution of CPU and MMAE be leveraged to optimize the performance of applications that combine GEMM and non-GEMM workloads

The heterogeneous nature of MACO, with its parallel execution of CPU and MMAE, presents an opportunity to optimize the performance of applications that combine GEMM and non-GEMM workloads. By leveraging the parallel computing capabilities of both components, MACO can effectively distribute the computational load between the CPU cores and MMAE, maximizing overall system throughput. To optimize performance, task scheduling algorithms can be implemented to dynamically allocate GEMM and non-GEMM tasks to the most suitable processing units based on workload characteristics and resource availability. Furthermore, efficient data sharing mechanisms and synchronization protocols can be employed to facilitate seamless communication between the CPU and MMAE, ensuring coordinated execution of diverse workloads. By intelligently orchestrating the parallel execution of GEMM and non-GEMM tasks, MACO can achieve superior performance and efficiency in handling complex computational workloads.