75 citations · 206 across the 28 of their papers we have counts for
5 papers · 1 filter
Evaluation of STT-MRAM as a Scratchpad for Training in ML Accelerators
Sourjya Roy, Cheng Wang, Anand Raghunathan
Progress in artificial intelligence and machine learning over the past decade has been driven by the ability to train larger deep neural networks (DNNs), leading to a compute deman…
Approximate Computing and the Efficient Machine Learning Expedition
Jörg Henkel, Hai Li, Anand Raghunathan +4
Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC researc…
GNNerator: A Hardware/Software Framework for Accelerating Graph Neural Networks
Jacob R. Stevens, Dipankar Das, Sasikanth Avancha +2
Graph Neural Networks (GNNs) use a fully-connected layer to extract features from the nodes of a graph and aggregate these features using message passing between nodes, combining t…
Softermax: Hardware/Software Co-Design of an Efficient Softmax for Transformers
Jacob R. Stevens, Rangharajan Venkatesan, Steve Dai +2
Transformers have transformed the field of natural language processing. This performance is largely attributed to the use of stacked self-attention layers, each of which consists o…
Hardware Accelerated Power Estimation
Joel Coburn, Srivaths Ravi, Anand Raghunathan
In this paper, we present power emulation, a novel design paradigm that utilizes hardware acceleration for the purpose of fast power estimation. Power emulation is based on the obs…