Zobrazeno 1 - 10
of 660
pro vyhledávání: '"Nair, Prashant"'
Quantum computers face challenges due to limited resources, particularly in cloud environments. Despite these obstacles, Variational Quantum Algorithms (VQAs) are considered promising applications for present-day Noisy Intermediate-Scale Quantum (NIS
Externí odkaz:
http://arxiv.org/abs/2409.12432
Publikováno v:
In Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 2024 Apr 27 (pp. 980-998)
The Quantum Approximate Optimization Algorithm (QAOA) addresses combinatorial optimization challenges by converting inputs to graphs. However, the optimal parameter searching process of QAOA is greatly affected by noise. Larger problems yield bigger
Externí odkaz:
http://arxiv.org/abs/2407.14490
In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addresses both single-device and distributed pipeline and tensor model par
Externí odkaz:
http://arxiv.org/abs/2404.14632
Training recommendation models pose significant challenges regarding resource utilization and performance. Prior research has proposed an approach that categorizes embeddings into popular and non-popular classes to reduce the training time for recomm
Externí odkaz:
http://arxiv.org/abs/2404.04270
Autor:
Adnan, Muhammad, Arunkumar, Akhil, Jain, Gaurav, Nair, Prashant J., Soloveychik, Ilya, Kamath, Purushotham
Publikováno v:
Proceedings of the 7th Annual Conference on Machine Learning and Systems (MLSys), 2024
Transformers have emerged as the underpinning architecture for Large Language Models (LLMs). In generative language models, the inference process involves two primary phases: prompt processing and token generation. Token generation, which constitutes
Externí odkaz:
http://arxiv.org/abs/2403.09054
Autor:
Fang, Bo, Li, Xinyi, Dam, Harvey, Tan, Cheng, Hari, Siva Kumar Sastry, Tsai, Timothy, Laguna, Ignacio, Tao, Dingwen, Gopalakrishnan, Ganesh, Nair, Prashant, Barker, Kevin, Li, Ang
Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet such demand, one of the critical features of machine-learning-specific accelerators such as NVIDIA Tensor Cores, AMD Matrix Cores, and Google TPUs is th
Externí odkaz:
http://arxiv.org/abs/2311.05782
Recommendation models are vital in delivering personalized user experiences by leveraging the correlation between multiple input features. However, deep learning-based recommendation models often face challenges due to evolving user behaviour and ite
Externí odkaz:
http://arxiv.org/abs/2308.14902
Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environm
Externí odkaz:
http://arxiv.org/abs/2307.02623
Publikováno v:
The 29th IEEE International Symposium on High-Performance Computer Architecture (HPCA 2022)
As Dynamic Random Access Memories (DRAM) scale, they are becoming increasingly susceptible to Row Hammer. By rapidly activating rows of DRAM cells (aggressor rows), attackers can exploit inter-cell interference through Row Hammer to flip bits in neig
Externí odkaz:
http://arxiv.org/abs/2212.12613
Autor:
Tannu, Swamit, Nair, Prashant J.
Publikováno v:
Energy Informatics Review (Volume 3 Issue 3, October 2023)
Scalable Solid-State Drives (SSDs) have ushered in a transformative era in data storage and accessibility, spanning both data centers and portable devices. However, the strides made in scaling this technology can bear significant environmental conseq
Externí odkaz:
http://arxiv.org/abs/2207.10793