5 papers
Eidola: Modeling Multi-GPU Network Communication Traffic in Distributed AI Workloads
Ranganath R. Selagamsetty, Matthew Poremba, Bradford M. Beckmann +2
As distributed AI workloads grow in scale, multi-GPU systems have become essential for training large models. Although techniques like kernel fusion and overlapping communication w…
Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates
Kyle Daruwalla, Mikko Lipasti
Artificial neural networks have successfully tackled a large variety of problems by training extremely deep networks via back-propagation. A direct application of back-propagation…
Accelerating Deep Learning with Dynamic Data Pruning
Ravi S Raju, Kyle Daruwalla, Mikko Lipasti
Deep learning's success has been attributed to the training of large, overparameterized models on massive amounts of data. As this trend continues, model training has become prohib…
MicroGrad: A Centralized Framework for Workload Cloning and Stress Testing
Gokul Subramanian Ravi, Ramon Bertran, Pradip Bose +1
We present MicroGrad, a centralized automated framework that is able to efficiently analyze the capabilities, limits and sensitivities of complex modern processors in the face of c…
BlurNet: Defense by Filtering the Feature Maps
Ravi Raju, Mikko Lipasti
Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemm…