Convolutional neural networkCNN pruningcompressed CNNHardware accelerationFPGAIn this paper, we propose a novel Convolutional Neural Network hardware accelerator called CoNNA, capable of accelerating pruned, quantized CNNs. In contrast to most existing solutions, CoNNA offers a complete solution to the ...
Field-programmable gate array (FPGA) has become an excellent hardware accelerator solution for convolutional neural networks (CNNs). Meanwhile, optimizing methods, such as model compression, have been proposed. As most CNN accelerators focus on dense neural networks, to solve the problem of difficult...
论文分享—A Compiler for Automatic Selection of Suitable Processing-in-Memory Instruc 编译行 277 0 论文分享—TC-CIM Empowering Tensor Comprehensions for Computing-In-Memory 编译行 143 0 论文分享—Compiling Neural Networks for a Computational Memory Accelerator 编译行 179 0 ...
Design of Hardware Accelerator for Artificial Neural Networks Using Multi-operand AdderComputational requirements of Artificial Neural Networks (ANNs) are so vastly different from the conventional architectures that exploring new computing paradigms, hardware architectures, and their......
Finally, we present a systematic benchmarking analysis on a tensor processing unit (TPU)-like AI accelerator in the edge and in the cloud and evaluate the use of these emerging memories. Key points The global buffer in artificial intelligence (AI) hardware (for example, the tensor processing ...
To address these issues, this paper takes the YOLO-V3 target detection algorithm as an example, introducing the hierarchical structure of the YOLO-V3 network, analyzing acceleration methods for each layer in the YOLO-V3 network, designing a convolutional neural network accelerator, and comparing its...
In artificial intelligence, the large role is played by machine learning (ML) in a variety of applications. This article aims at providing a comprehensive survey on summarizing recent trends and advances in hardware accelerator design for machine learning based on various hardware platforms like ASIC...
那么为什么能叫glow,glow其实是graph+lowering的简写,意思就是说,通过这个低阶的IR,从而在针对大量不同的上层model中的op到下层不同hardware accelerator的实现都尽可能通过一些比较简单的线性代数源语来实现,有点类似精简指令集的感觉。 为什么要做deep learning compiler,其实motivation很简单。我们现在经常用的深度学习...
BrainChip's spiking neural network technology is unique in its ability to provide outstanding performance while avoiding the math intensive, power hungry, and high-cost downsides of deep learning in convolutional neural networks." BrainChip Accelerator is compatible with Windows or Linux computing ...
However, because of intensive matrix computations and complicated data flow being involved, the hardware design for the Transformer model has never been reported. In this paper, we propose the first hardware accelerator for two key components, i.e., the multi-head attention (MHA) ResBlock and ...