A VM-HDL Co-Simulation Framework for Systems with PCIe-Connected FPGAs
- URL: http://arxiv.org/abs/2501.14815v1
- Date: Sun, 19 Jan 2025 22:06:36 GMT
- Title: A VM-HDL Co-Simulation Framework for Systems with PCIe-Connected FPGAs
- Authors: Shenghsun Cho, Mrunal Patel, Basavaraj Kaladagi, Han Chen, Tapti Palit, Michael Ferdman, Peter Milder,
- Abstract summary: It is challenging to jointly develop and debug host software and FPGA hardware.<n>Changes to the hardware design require a time-consuming FPGA synthesis process.<n>A VM-HDL co-simulation framework is designed to run the same software, operating system, and hardware designs as the target physical system.
- Score: 7.519011820592022
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: PCIe-connected FPGAs are gaining popularity as an accelerator technology in data centers. However, it is challenging to jointly develop and debug host software and FPGA hardware. Changes to the hardware design require a time-consuming FPGA synthesis process, and modification to the software, especially the operating system and device drivers, can frequently cause the system to hang, without providing enough information for debugging. The combination of these problems results in long debug iterations and a slow development process. To overcome these problems, we designed a VM-HDL co-simulation framework, which is capable of running the same software, operating system, and hardware designs as the target physical system, while providing full visibility and significantly shorter debug iterations.
Related papers
- Bridging the Gap: Physical PCI Device Integration Into SystemC-TLM Virtual Platforms [0.16492989697868893]
Virtual Platforms (VPs) serve as a platform to execute and debug the unmodified target software at an early design stage.<n>VPs need to provide high simulation speed to ensure the target software executes within a reasonable time.<n>This paper introduces a novel approach for integrating real Peripheral Component Interconnect ( PCI) devices into SystemC-TLM-2.0-based VPs.
arXiv Detail & Related papers (2025-05-21T14:46:41Z) - FPGA-based Acceleration for Convolutional Neural Networks: A Comprehensive Review [3.7810245817090906]
Convolutional Neural Networks (CNNs) are fundamental to deep learning, driving applications across various domains.<n>This paper provides a comprehensive review of FPGA-based hardware accelerators specifically designed for CNNs.
arXiv Detail & Related papers (2025-05-04T04:03:37Z) - Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning [49.997801914237094]
We introduce the Fire-Flyer AI- HPC architecture, a synergistic hardware-software co-design framework and its best practices.
For Deep Learning (DL) training, we deployed the Fire-Flyer 2 with 10,000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%.
Through our software stack, including HaiScale, 3FS, and HAI-Platform, we achieved substantial scalability by overlapping computation and communication.
arXiv Detail & Related papers (2024-08-26T10:11:56Z) - Designing and Implementing a Generator Framework for a SIMD Abstraction Library [53.84310825081338]
We present TSLGen, a novel end-to-end framework for generating an SIMD abstraction library.
We show that our framework is comparable to existing libraries, and we achieve the same performance results.
arXiv Detail & Related papers (2024-07-26T13:25:38Z) - CHOSEN: Compilation to Hardware Optimization Stack for Efficient Vision Transformer Inference [4.523939613157408]
Vision Transformers (ViTs) represent a groundbreaking shift in machine learning approaches to computer vision.
This paper introduces CHOSEN, a software-hardware co-design framework to address these challenges and offer an automated framework for ViT deployment on the FPGAs.
ChoSEN achieves a 1.5x and 1.42x improvement in the throughput on the DeiT-S and DeiT-B models.
arXiv Detail & Related papers (2024-07-17T16:56:06Z) - Enhancing Dropout-based Bayesian Neural Networks with Multi-Exit on FPGA [20.629635991749808]
This paper proposes an algorithm and hardware co-design framework that can generate field-programmable gate array (FPGA)-based accelerators for efficient BayesNNs.
At the algorithm level, we propose novel multi-exit dropout-based BayesNNs with reduced computational and memory overheads.
At the hardware level, this paper introduces a transformation framework that can generate FPGA-based accelerators for the proposed efficient BayesNNs.
arXiv Detail & Related papers (2024-06-20T17:08:42Z) - The Emergence of Hardware Fuzzing: A Critical Review of its Significance [0.4943822978887544]
Hardware fuzzing, inspired by software testing methodologies, has gained prominence for its efficacy in identifying bugs within complex hardware designs.
Despite the introduction of various hardware fuzzing techniques, obstacles such as inefficient conversion of hardware modules into software models impede their effectiveness.
This work examines the reliability of existing hardware fuzzing techniques in identifying vulnerabilities and identifies research gaps for future advancements in design verification techniques.
arXiv Detail & Related papers (2024-03-19T15:12:11Z) - FusionAI: Decentralized Training and Deploying LLMs with Massive
Consumer-Level GPUs [57.12856172329322]
We envision a decentralized system unlocking the potential vast untapped consumer-level GPU.
This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
arXiv Detail & Related papers (2023-09-03T13:27:56Z) - Virtualization of Tiny Embedded Systems with a robust real-time capable
and extensible Stack Virtual Machine REXAVM supporting Material-integrated
Intelligent Systems and Tiny Machine Learning [0.0]
This paper shows and evaluates the suitability of the proposed VM architecture for operationally equivalent software and hardware (FPGA) implementations.
In a holistic architecture approach, the VM specifically addresses digital signal processing and tiny machine learning.
arXiv Detail & Related papers (2023-02-17T17:13:35Z) - Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and
Algorithm Co-design [66.39546326221176]
Attention-based neural networks have become pervasive in many AI tasks.
The use of the attention mechanism and feed-forward network (FFN) demands excessive computational and memory resources.
This paper proposes a hardware-friendly variant that adopts a unified butterfly sparsity pattern to approximate both the attention mechanism and the FFNs.
arXiv Detail & Related papers (2022-09-20T09:28:26Z) - FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task.
The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources.
It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
arXiv Detail & Related papers (2022-01-18T13:59:22Z) - VAQF: Fully Automatic Software-hardware Co-design Framework for Low-bit
Vision Transformer [121.85581713299918]
We propose VAQF, a framework that builds inference accelerators on FPGA platforms for quantized Vision Transformers (ViTs)
Given the model structure and the desired frame rate, VAQF will automatically output the required quantization precision for activations.
This is the first time quantization has been incorporated into ViT acceleration on FPGAs.
arXiv Detail & Related papers (2022-01-17T20:27:52Z) - Resistive Neural Hardware Accelerators [0.46198289193451136]
ReRAM-based in-memory computing has great potential in the implementation of area and power efficient inference.
The shift towards ReRAM-based in-memory computing has great potential in the implementation of area and power efficient inference.
In this survey, we review the state-of-the-art ReRAM-based Deep Neural Networks (DNNs) many-core accelerators.
arXiv Detail & Related papers (2021-09-08T21:11:48Z) - Towards High Performance Java-based Deep Learning Frameworks [0.22940141855172028]
Modern cloud services have set the demand for fast and efficient data processing.
This demand is common among numerous application domains, such as deep learning, data mining, and computer vision.
In this paper we have employed TornadoVM, a state-of-the-art programming framework to transparently accelerate Deep Netts; a Java-based deep learning framework.
arXiv Detail & Related papers (2020-01-13T13:03:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.