Optimizing GEMM Acceleration on Leading AI-Optimized FPGAs: Versal ACAP and Stratix 10 NX

المفاهيم الأساسية

This work presents novel systematic frameworks to optimize the performance of General Matrix Multiplication (GEMM), a fundamental operation in Deep Learning workloads, by exploiting the unique and distinct architectural characteristics of the Versal ACAP and Stratix 10 NX FPGA platforms.

الملخص

The paper focuses on efficiently processing and analyzing GEMM workloads on the Versal ACAP and Stratix 10 NX FPGA platforms. It makes the following key contributions:

For the Versal ACAP, the authors leverage the state-of-the-art MaxEVA framework and extend it to incorporate an additional memory hierarchy level utilizing the Versal FPGA's on-chip resources. They maximize performance via design space exploration (DSE) and analytical modeling, and propose a novel RAM optimization scheme to overcome limitations of Vitis High-Level Synthesis (HLS).

For the Stratix 10 NX, the authors develop a novel framework to design, map and optimize a configurable GEMM accelerator by exploiting the device's in-fabric Tensor Blocks (TBs). Their framework involves extensive DSE and analytical modeling to maximize GEMM performance.

The authors demonstrate their frameworks on various GEMM workloads for int8 precision, showing throughput up to 77 and 68 TOPs with 100% AIE and 91% TB utilization for Versal and Stratix, respectively. They achieve up to 0.94 and 1.35 TOPs/W energy efficiency, with 88% and 94% on-chip memory for Versal and Stratix, respectively.

The paper provides notable insights and guidelines for GEMM optimization, programmability aspects, architectural attributes, and limitations on both AI-optimized FPGAs.

الإحصائيات

The Versal VC1902 ACAP has a theoretical peak throughput of 135 TOPs (int8), while the Stratix 10 NX 2100 has a peak of 143 TOPs (int8).
The Versal VC1902 has a peak DRAM bandwidth of 102.4 GB/s, while the Stratix 10 NX has 512 GB/s.
The Versal VC1902 is manufactured in a 7nm TSMC process, while the Stratix 10 NX uses a 14nm Intel process.

اقتباسات

"FPGAs are a promising platform for accelerating Deep Learning (DL) applications, due to their high performance, low power consumption, and reconfigurability."
"The two major FPGA vendors have adopted different directions in optimizing their FPGAs for DL. AMD/Xilinx introduced the Versal Adaptive Compute Acceleration Platform (ACAP), comprising the novel AI Engine (AIE), along with reconfigurable logic (FPGA) and scalar processors (CPUs). In contrast, Intel released the Stratix 10 NX, maintaining the existing FPGA architecture, but replacing legacy DSP blocks with new AI Tensor Blocks (TBs)."

الرؤى الأساسية المستخلصة من

Efficient Approaches for GEMM Acceleration on Leading AI-Optimized FPGAs

by Endri Taka,D... في arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11066.pdf

استفسارات أعمق

What are the key architectural differences between the Versal ACAP and Stratix 10 NX that necessitate distinct optimization approaches for GEMM acceleration

The key architectural differences between the Versal ACAP and Stratix 10 NX that require distinct optimization approaches for GEMM acceleration stem from their unique design characteristics.

Versal ACAP:

Architecture: Versal ACAP comprises the AIE array, PL, and PS components. The AIE array consists of programmable vector processors with high parallelism levels, while the PL includes FPGA resources like LUTs, FFs, and DSP slices. The AIE array communicates efficiently with the PL through AIE-PL tiles.
Memory Hierarchy: Versal ACAP utilizes on-chip memory resources like BRAMs and URAMs for data storage and processing.
Programming Model: Versal ACAP allows high-level programming using tools like Vitis HLS and V++ for AIE-PL integration.

Stratix 10 NX:

Architecture: Stratix 10 NX features AI Tensor Blocks (TBs) that replace traditional DSP blocks, offering specialized dot-product engines for deep learning operations. The TBs operate in a cascade loading mode and are arranged in chains for efficient data processing.
Memory Architecture: Stratix 10 NX uses M20K blocks for on-chip memory, with a focus on optimizing data movement and processing within the TB arrays.
Programming Model: Stratix 10 NX requires RTL-level programming for designing and implementing GEMM accelerators, with a focus on optimizing data flow and memory access patterns.

The distinct architectural features of Versal ACAP and Stratix 10 NX, such as the AIE array vs. TBs, memory hierarchy, and programming models, necessitate tailored optimization strategies to maximize GEMM performance on each platform.

How can the insights and guidelines provided in this work be applied to optimize the performance of other deep learning operations beyond GEMM on these AI-optimized FPGA platforms

The insights and guidelines provided in this work can be extrapolated to optimize the performance of other deep learning operations beyond GEMM on Versal ACAP and Stratix 10 NX AI-optimized FPGA platforms in the following ways:

Memory Optimization: The systematic methodologies for optimizing on-chip memory usage and data reuse can be applied to other deep learning operations to enhance efficiency and reduce off-chip bandwidth requirements.

Architecture-Specific Design: Understanding the unique architectural attributes of each platform can guide the development of specialized accelerators for different deep learning tasks, leveraging the strengths of the AIE array in Versal and the TBs in Stratix 10 NX.

DSE and Analytical Modeling: Utilizing design space exploration and analytical modeling techniques can help identify optimal configurations for various deep learning operations, ensuring high throughput and energy efficiency.

Automatic Code Generation: Developing tools for automatic RTL code generation, as demonstrated in this work, can streamline the implementation of custom accelerators for different deep learning tasks on AI-optimized FPGAs.

By applying the principles of architecture-specific optimization, memory efficiency, and automated design generation, similar performance enhancements can be achieved for a wide range of deep learning operations on Versal ACAP and Stratix 10 NX platforms.

What are the potential future directions for further improving the programmability and ease-of-use of these AI-optimized FPGA architectures for deep learning workloads

To further improve the programmability and ease-of-use of AI-optimized FPGA architectures for deep learning workloads, the following future directions can be explored:

Higher-Level Abstractions: Develop higher-level programming models and tools that abstract the complexities of FPGA programming, making it more accessible to deep learning practitioners without extensive hardware design expertise.

Optimization Libraries: Create specialized libraries and frameworks tailored for deep learning tasks on AI-optimized FPGAs, offering pre-optimized modules for common operations to simplify development and accelerate deployment.

Automated Optimization Tools: Enhance automated optimization tools that can intelligently analyze deep learning workloads, recommend optimal configurations, and generate efficient RTL code, reducing the manual effort required for FPGA acceleration.

Integration with DL Frameworks: Integrate FPGA acceleration seamlessly with popular deep learning frameworks like TensorFlow and PyTorch, providing native support for deploying models on AI-optimized FPGA platforms.

By focusing on these future directions, the programmability and usability of AI-optimized FPGA architectures for deep learning applications can be enhanced, enabling more efficient and scalable deployment of neural network models on FPGA hardware.