Archive of posts tagged: Machine Learning
Deep Neural Network Acceleration Beyond Chips
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...
Sure, We Have Systems For ML But How About ML For 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...
Hardware Acceleration Opportunities for Machine Learning on Massive Datasets
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...
Why the GPGPU is Less Efficient than the TPU for DNNs
The GPGPU’s massive multithreading is unnecessary for DNNs, and imposes performance, area, and energy overheads. By avoiding such multithreading, the TPU is more efficient.
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