Results 11 to 20 of about 1,048,263 (336)

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware [PDF]

open access: yesRobotics: Science and Systems, 2023
Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback.
Tony Zhao   +3 more
semanticscholar   +1 more source

Chip-Chat: Challenges and Opportunities in Conversational Hardware Design [PDF]

open access: yesWorkshop on Machine Learning for CAD, 2023
Modern hardware design starts with specifications provided in natural language. These are then translated by hardware engineers into appropriate Hardware Description Languages (HDLs) such as Verilog before synthesizing circuit elements.
Jason Blocklove   +3 more
semanticscholar   +1 more source

TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for Embeddings [PDF]

open access: yesInternational Symposium on Computer Architecture, 2023
In response to innovations in machine learning (ML) models, production workloads changed radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its third supercomputer for such ML models. Optical circuit switches (OCSes)
N. Jouppi   +13 more
semanticscholar   +1 more source

Hardware error correction for programmable photonics [PDF]

open access: yesarXiv.org, 2021
Programmable photonic circuits of reconfigurable interferometers can be used to implement arbitrary operations on optical modes, facilitating a flexible platform for accelerating tasks in quantum simulation, signal processing, and artificial intelligence.
S. Bandyopadhyay, R. Hamerly, D. Englund
semanticscholar   +1 more source

Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets [PDF]

open access: yesNature, 2017
Quantum computers can be used to address electronic-structure problems and problems in materials science and condensed matter physics that can be formulated as interacting fermionic problems, problems which stretch the limits of existing high-performance
A. Kandala   +6 more
semanticscholar   +1 more source

HAT: Hardware-Aware Transformers for Efficient Natural Language Processing [PDF]

open access: yesAnnual Meeting of the Association for Computational Linguistics, 2020
Transformers are ubiquitous in Natural Language Processing (NLP) tasks, but they are difficult to be deployed on hardware due to the intensive computation.
Hanrui Wang   +6 more
semanticscholar   +1 more source

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search [PDF]

open access: yesComputer Vision and Pattern Recognition, 2018
Designing accurate and efficient ConvNets for mobile devices is challenging because the design space is combinatorially large. Due to this, previous neural architecture search (NAS) methods are computationally expensive.
Bichen Wu   +9 more
semanticscholar   +1 more source

A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration

open access: yesElectronics, 2022
Over the past decade, deep-learning-based representations have demonstrated remarkable performance in academia and industry. The learning capability of convolutional neural networks (CNNs) originates from a combination of various feature extraction ...
Deepak Ghimire   +2 more
semanticscholar   +1 more source

HAQ: Hardware-Aware Automated Quantization With Mixed Precision [PDF]

open access: yesComputer Vision and Pattern Recognition, 2018
Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a ...
Kuan Wang   +4 more
semanticscholar   +1 more source

Research on the Construction of Virtual Simulation Training System for Intelligent Manufacturing Based on Outcomes - Based Education Concept [PDF]

open access: yesSHS Web of Conferences, 2023
Outcomes-Based Education (OBE) is the core concept of professional accreditation in engineering education, and is a teaching and learning approach that determines teaching strategies based on learning outcomes.
Zhao JiaQi   +6 more
doaj   +1 more source

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