Neural network architectureCNNDesign space explorationOwing to good performance, deep Convolution Neural Networks (CNNs) are rapidly rising in popularity across a broad range of applications. Since high accuracy CNNs are both computation intensive and memory intensive, many researchers have shown ...
Bojnordi, M. N., & Ipek, E. (2016). Memristive boltzmann machine: A hardware accelerator for combinatorial optimization and deep learning. In2016 IEEE International Symposium on High Performance Computer Architecture (HPCA)(pp. 1–13). IEEE. ...
Hardware architecture: VTA是一个参数化的加速器,其可以对深度学习的计算图进行加速。 VTA被使用二阶段编程接口的compiler stack显示的进行编程 被GEMM core,SRAM大小,data type的深度参数化 VTA ARCHITECTURE AND JIT RUNTIME VTA硬件架构以及JIT compiler and runtime的软件架构共同设计以成功实现一个灵活的深度学习...
1. Identify the bottleneck for AI and its implementation on different platform 2. Develop appropriate computing theory, architecture, number system and quantization approach to improve the performance of AI applications. 3. Optimizing architecture specifically for the most computing intensive part ...
Design and Analysis of a Novel Finite-Time Convergent and Noise-Tolerant Recurrent Neural Network for Time-Variant Matrix Inversion 2020, IEEE Transactions on Systems, Man, and Cybernetics: Systems A Reversible-Logic based Architecture for Artificial Neural Network 2020, Midwest Symposium on Circuits an...
In machine learning, DBN is a generative graphical model, or alternatively a type of deep neural network, composed of multiple layers of restricted Boltzmann machines. This hardware system is based on what we call Neuron Machine (NM) hardware architecture that can be used specifically for ...
Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmark...
Hardware for machine learning: Challenges and opportunities[C]. Custom Integrated Circuits Conference (CICC). IEEE, 2018: 1-8. Chen Y H, Emer J, Sze V. Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks[C]. ACM SIGARCH Computer Architecture News. ...
In this paper, we propose a novel hardware architecture for RL agents based on the learning hierarchical policies method. We show that hierarchical learning with several levels of control improves RL agents training efficiency and the agent converges faster compared to a none hierarchical model and ...
4. System architecture The processing pipeline for the inference of a generic single-stage neural network for object detection can be divided into three main phases, as shown in Fig. 3. In the pre-processing phase, the input frame is elaborated to match the input specifications of the convolut...