Results 281 to 290 of about 649,011 (340)
Some of the next articles are maybe not open access.
ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars
International Symposium on Computer Architecture, 2016A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs).
A. Shafiee +7 more
semanticscholar +1 more source
Scalable and Conflict-Free NTT Hardware Accelerator Design: Methodology, Proof, and Implementation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023Number theoretic transform (NTT) is useful for the acceleration of polynomial multiplication, which is the main performance bottleneck in the next-generation cryptographic schemes.
Jianan Mu +10 more
semanticscholar +1 more source
SCNN: An accelerator for compressed-sparse convolutional neural networks
International Symposium on Computer Architecture, 2017Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of CNNs, especially in mobile platforms such as autonomous vehicles, cameras ...
A. Parashar +8 more
semanticscholar +1 more source
A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm
IEEE Journal of Solid-State Circuits, 2023The energy efficiency of deep neural network (DNN) inference can be improved with custom accelerators. DNN inference accelerators often employ specialized hardware techniques to improve energy efficiency, but many of these techniques result in ...
Ben Keller +8 more
semanticscholar +1 more source
Timeloop: A Systematic Approach to DNN Accelerator Evaluation
IEEE International Symposium on Performance Analysis of Systems and Software, 2019This paper presents Timeloop, an infrastructure for evaluating and exploring the architecture design space of deep neural network (DNN) accelerators. Timeloop uses a concise and unified representation of the key architecture and implementation attributes
A. Parashar +9 more
semanticscholar +1 more source
SSRN Electronic Journal, 2004 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hausmann, Ricardo +2 more
openaire +2 more sources
ViA: A Novel Vision-Transformer Accelerator Based on FPGA
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022Since Google proposed Transformer in 2017, it has made significant natural language processing (NLP) development. However, the increasing cost is a large amount of calculation and parameters.
Teng Wang +6 more
semanticscholar +1 more source
Accelerating virtualization of accelerators
2021The use of specialized accelerators is among the most promising paths to better energy efficiency for computationally heavy workloads. However, current software and system support for accelerators is limited, and no production-ready solutions have yet been provided for accelerators to be efficiently accessed or shared in domains such as cloud ...
Yu, Hangchen, 0000-0002-6515-6271
openaire +2 more sources
PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference
International Conference on Architectural Support for Programming Languages and Operating Systems, 2019Memristor crossbars are circuits capable of performing analog matrix-vector multiplications, overcoming the fundamental energy efficiency limitations of digital logic. They have been shown to be effective in special-purpose accelerators for a limited set
Aayush Ankit +10 more
semanticscholar +1 more source
An integrated large-scale photonic accelerator with ultralow latency
NatureIntegrated photonics, particularly silicon photonics, have emerged as cutting-edge technology driven by promising applications such as short-reach communications, autonomous driving, biosensing and photonic computing1, 2, 3–4.
Shiyue Hua +25 more
semanticscholar +1 more source