Cerebras Systems Enables Brain-Scale AI
Cambrian AI Research principal analyst Karl Freund explores Cerebras Systems' approach to brain-scale AI and the new technologies that enable it.
September 21, 2021
Deep Learning Programming at Scale
Deep learning has become one of the most important computational workloads of our generation, advancing applications across industries from healthcare to autonomous driving. But it is also profoundly computationally intensive.
June 29, 2021
Stream-AI-MD: streaming AI-driven adaptive molecular simulations for heterogeneous computing platforms
Emerging hardware tailored for artificial intelligence (AI) and machine learning (ML) methods provide novel means to couple them with traditional high performance computing (HPC) workflows involving molecular dynamics (MD) simulations. We propose Stream-AI-MD, a novel instance of applying deep learning methods to drive adaptive MD simulation campaigns in a streaming manner.
July 5, 2021
Alexander Brace, Michael Salim, Vishal Subbiah, Heng Ma, Murali Emani, Anda Trifa, Austin R. Clyde, Corey Adams, Thomas Uram, Hyunseung Yoo, Andew Hock, Jessica Liu, Venkatram Vishwanath, Arvind Ramanathan
Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation
We propose combining memory saving techniques with traditional U-Net architectures to increase the complexity of the models on the Brain Tumor Segmentation (BraTS) challenge. The BraTS challenge consists of a 3D segmentation of a 240 240 155 4 input image into a set of tumor classes.
March 5, 2021
Mihir Pendse, Vithursan Thangarasa, Vitaliy Chiley, Ryan Holmdahl, Joel Hestness, Dennis DeCoste
Pipelined Backpropagation at Scale: Training Large Models without Batches
New hardware can substantially increase the speed and efficiency of deep neural network training. To guide the development of future hardware architectures, it is pertinent to explore the hardware and machine learning properties of alternative training algorithms.
March 1, 2021
Atli Kosson, Vitaliy Chiley, Abhinav Venigalla, Joel Hestness, Urs Koster