The design of FPGA based accelerator design traditionally required a tedious Register Transfer Level (RTL) design flow process. To improve design productivity, the proposed work uses High-Level Synthesis (HLS), described in OpenCL, to generate the FPGA bitstream for the CNN model. The 2D ...
Recently, FPGA-based CNN accelerators have demonstrated superior energy efficiency compared to high-performance devices like GPGPUs. However, due to the constrained on-chip resource and many other factors, single-board FPGA designs may have difficulties in achieving optimal energy efficien...
2 DeepFire2: A Convolutional Spiking Neural Network Accelerator on FPGAs 标题:DeepFire2:FPGA 上的卷积尖峰神经网络加速器 文章链接:arxiv.org/abs/2305.0518 摘要:类脑脉冲神经网络 (SNN) 通过集成和激发神经元取代传统神经网络的乘法累加运算,目的是实现更高的能效。这些神经元的专用硬件实现在功率和性能方面...
preventing them from being deployed on edge devices or in resource-constrained environments such as Internet of Things (IoT) systems3,4,5. Recent attempts to compress and speed natural language processing (NLP) paradigms on embedded systems like field programmable gateway arrays (FPGAs) have been ...
The adoption of transformer networks has experienced a notable surge in various AI applications. However, the increased computational complexity, stemming primarily from the self-attention mechanism, parallels the manner in which convolution operations c
It is, however, still a challenge to accelerate large-scale CNNs [15] on an FPGA as model parameters typically require far more memory than the on-chip capacity of the FPGAs. Another challenge is to find an optimal configuration for a given HW accelerator design due to the long design ...
In this work, we made two contributions: (1) Proposed a new neighbor sampler: CONCAT Sampler, which can be easily accelerated on hardware level while guaranteeing the test accuracy. (2) Designed a CONCAT-sampler-accelerator based on FPGA, with which the neighbor sampling process boosted to ...
2017.12-A survey of FPGA-based neural network accelerator 2018-FITEE-Recent Advances in Efficient Computation of Deep Convolutional Neural Networks 2018-IEEE Signal Processing Magazine-Model compression and acceleration for deep neural networks: The principles, progress, and challenges. Arxiv extension 2018...
To address the issue of computational efficiency degradation in existing designs for supporting large-kernel convolutions, an FPGA-based inference accelerator is proposed for the efficient deployment of CNNs with arbitrary kernel sizes. Firstly, a Z-flow method is presented to optimize the computing ...
we present a new efficient OpenCL-based Accelerator for large scale Convolutional Neural Networks called “Fast Inference on FPGAs for Convolution Neural Network” (FFCNN). FFCNN is based on a deeply pipelined OpenCL kernels architecture. As pointed out before, high-level synthesis tools such as...