AI
An Early Look at Baidu’s Custom AI and Analytics Processor
In the U.S. it is easy to focus on our native hyperscale companies (Google, Amazon, Facebook, etc.) …
deep learning
AI
IBM Highlights PowerAI, OpenPower System Scalability
The golden grail of deep learning has two handles. On the one hand, developing and scaling systems that can train ever-growing model sizes is one concern. …
AI
Google Research Pushing Neural Networks Out of the Datacenter
Google has been at the bleeding edge of AI hardware development with the arrival of its TPU and other system-scale modifications to make large-scale neural network processing efficient and fast. …
AI
Wrenching Efficiency Out of Custom Deep Learning Accelerators
Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. …
AI
Technology Requirements for Deep and Machine Learning
Having been at the forefront of machine learning since the 1980s when I was a staff scientist in the Theoretical Division at Los Alamos performing basic research on machine learning (and later applying it in many areas including co-founding a machine-learning based drug discovery company), I was lucky enough to participate in the creation and subsequently to observe first-hand the process by which the field of machine-learning grew to become a ‘bandwagon’ that eventually imploded due to misconceptions about the technology and what it could accomplish. …
AI
When Will AI Replace Traditional Supercomputing Simulations?
The science fiction of a generation ago predicted a future in which humans were replaced by the reasoning might of a supercomputer. …
AI
The Next Battleground for Deep Learning Performance
The frameworks are in place, the hardware infrastructure is robust, but what has been keeping machine learning performance at bay has far less to do with the system-level capabilities and more to do with intense model optimization. …
AI
Escher Erases Batching Lines for Efficient FPGA Deep Learning
Aside from the massive parallelism available in modern FPGAs, there are other two other key reasons why reconfigurable hardware is finding a fit in neural network processing in both training and inference. …
AI
Taking the Heavy Lifting Out of TensorFlow at Extreme Scale
There is no real middle ground when it comes to TensorFlow use cases. …
AI
A Look at Facebook’s Interactive Neural Network Visualization System
There has been much discussion about the “black box” problem of neural networks. …