by Carole-Jean Wu, David Brooks, Udit Gupta, Hsien-Hsin Lee, and Kim Hazelwood on Nov 7, 2019 | Tags: Accelerators, Benchmarks, Industry, Machine Learning
We live in the era of machine learning. As Geoff Hinton and Yann LeCun emphasized in their recent Turing lecture, the rise of machine learning (ML) was in great part facilitated by cheap and easy to use high-performance computing that has allowed ML models to be...
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by Hsien-Hsin Sean Lee on Aug 29, 2019 | Tags: Accelerators, Machine Learning, Wafer Scale Integration
There was stunning news at the HotChips-31 conference held at Stanford Memorial Auditorium last week. Cerebras, a startup company working on machine learning accelerators in stealth mode, finally came out and presented a groundbreaking accelerator chip packed with 1.2...
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by Mark D. Hill on Aug 23, 2019 | Tags: Accelerators, Emerging Technology, Machine Learning
This week Cerebras announced a bold design to accelerate deep neural networks with silicon that is not cut into chips. AI and Moore’s Law: Artificial Intelligence (AI) is much in the news for what it can do today and the promise of what it can do tomorrow (CCC/AAAI...
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by Christina Delimitrou on Jun 24, 2019 | Tags: Cloud computing, Machine Learning, Systems
The past few years have seen an unprecedented increase in the number of systems targeting machine learning (ML) applications and deep learning in particular (Jeff Dean has compiled a telling graph on the exponentially increasing number of ML papers). From hardware...
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by Spyros Blanas on Jun 3, 2019 | Tags: Databases, Machine Learning
Data management research has recently been paying more attention on how to run machine learning algorithms efficiently on massive datasets. This blog post focuses on three recent research papers that identify time-consuming data processing operations in machine...
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