Zobrazeno 1 - 4
of 4
pro vyhledávání: '"AbouElhamayed, Ahmed F."'
Autor:
Akhauri, Yash, AbouElhamayed, Ahmed F, Dotzel, Jordan, Zhang, Zhiru, Rush, Alexander M, Huda, Safeen, Abdelfattah, Mohamed S
The high power consumption and latency-sensitive deployments of large language models (LLMs) have motivated efficiency techniques like quantization and sparsity. Contextual sparsity, where the sparsity pattern is input-dependent, is crucial in LLMs b
Externí odkaz:
http://arxiv.org/abs/2406.16635
Deep neural network (DNN) inference has become an important part of many data-center workloads. This has prompted focused efforts to design ever-faster deep learning accelerators such as GPUs and TPUs. However, an end-to-end DNN-based vision applicat
Externí odkaz:
http://arxiv.org/abs/2403.12981
Autor:
AbouElhamayed, Ahmed F., Cui, Angela, Fernandez-Marques, Javier, Lane, Nicholas D., Abdelfattah, Mohamed S.
Conventional multiply-accumulate (MAC) operations have long dominated computation time for deep neural networks (DNNs), espcially convolutional neural networks (CNNs). Recently, product quantization (PQ) has been applied to these workloads, replacing
Externí odkaz:
http://arxiv.org/abs/2305.18334
Autor:
Fernandez-Marques, Javier, AbouElhamayed, Ahmed F., Lane, Nicholas D., Abdelfattah, Mohamed S.
Conventional multiply-accumulate (MAC) operations have long dominated computation time for deep neural networks (DNNs). Recently, product quantization (PQ) has been successfully applied to these workloads, replacing MACs with memory lookups to pre-co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65c3c1270d907cee75ae8b35a23b0f27
http://arxiv.org/abs/2305.18334
http://arxiv.org/abs/2305.18334