30.1: A 40nm VLIW Edge Accelerator with 5MB of 0.256pJ/b RRAM and a Localization Solver for Bristle Robot Surveillance 30.2: A 22nm 0.26nW/Synapse Spike-Driven Spiking Neural Network Processing Unit Using Time-Step-First Dataflow and Sparsity-Adaptive In-Memory Computing 30.3: VIP-Sat: A Bo...
IEEE Transactions on Wireless Communications, 19 [(2017) FlexFlow: A flexible dataflow accelerator architecture for convolutional neural networks Proceedings of the 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA’17). IEEE Computer Societ...
RNA: A Flexible and Efficient Accelerator Based on Dynamically Reconfigurable Computing for Multiple Convolutional Neural Networksdoi:10.1142/S0218126622502899CNNreconfigurable computingimage row broadcasting dataflowtile-by-tile computingzero detection technology...
Flexible dataflow scheduling is an effective methods to improve the computational performance of DNN as it can achieve better data reuse in the condition of limited bandwidth. The dataflow scheduling of convolution calculation in this paper is based on the analysis of the three patterns of DI∖DO...
We notice that a convolution neural network accelerator is investigated in (Li et al., 2021d) where a toolchain is demonstrated with high-accuracy block random access memory (BRAM)-aware FPGA oriented flexible structure. The accelerator called – HBDCA integrates the TFL for high-accuracy quantiz...
A BNN architecture and accelerator construction tool, permitting customization of throughput. BNN结构和加速创建工具 A range of prototypes that demonstrate the potential of BNNs on an off-the-shelf FPGAs platform. 原型展示 文章组织方式: 第二部分:CNN和BNN的背景及硬件实现 ...
When running on an Intel Stratix 10 280 FPGA, the Brainwave NPU achieves performance ranging from ten to over thirty-five teraflops, with no batching, on large, memory-intensive RNNs. Index Terms—neural network hardware; accelerator architec- tures; field programmable gate arrays I. INTRODUCTION...
使用 Keras,DL 开发者可以仅用几行代码就搭建起一个神经网络。此外,Keras 还可以和其它常用的 DL 包协同使用,例如 scikit-learn。然而,Keras 由于过度的封装导致其不够 flexible,在其中添加新算子或者得到 low-level 的 data information 都是很困难的。
[DATE 2020] [CKKS] A Flexible and Scalable NTT Hardware:Applications from Homomorphically Encrypted Deep Learning to Post-Quantum Cryptography. Mert A, Karabulut E, Aysu A, et al[Paper] [T-C 2020] [FHE] HEAWS: An Accelerator for Homomorphic Encryption on the Amazon AWS FPGA. ...
apache/mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more manuelruder/artistic-videos - Torch implementation for the paper "Artistic style transfer for videos" alibaba/weex...