536 citations · 905 across the 15 of their papers we have counts for
15 papers
Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration
Sherry Xu, Marco Heddes, Jackson Peng +14
We introduce Maia 200, an advanced AI accelerator delivering high performance-10 145 Tflop/s FP4 and 5072 Tflop/s FP8 within a 750W TDP and 7 TB/s HBM bandwidth. Maia exemplifies a…
VTC: DNN Compilation with Virtual Tensors for Data Movement Elimination
Muyan Hu, Ahan Gupta, Jiachen Yuan +7
With the widening gap between compute and memory operation latencies, data movement optimizations have become increasingly important for DNN compilation. Current optimizations such…
Blockbuster, Part 1: Block-level AI Operator Fusion
Ofer Dekel
Blockbuster is a framework for AI operator fusion in inference programs. The Blockbuster framework is compatible with any multiprocessor architecture that has a tiered memory hiera…
Learning SMaLL Predictors
Vikas K. Garg, Ofer Dekel, Lin Xiao
We present a new machine learning technique for training small resource-constrained predictors. Our algorithm, the Sparse Multiprototype Linear Learner (SMaLL), is inspired by the…
Adaptive Neural Networks for Efficient Inference
Tolga Bolukbasi, Joseph Wang, Ofer Dekel +1
We present an approach to adaptively utilize deep neural networks in order to reduce the evaluation time on new examples without loss of accuracy. Rather than attempting to redesig…
Linear Learning with Sparse Data
Ofer Dekel
Linear predictors are especially useful when the data is high-dimensional and sparse. One of the standard techniques used to train a linear predictor is the Averaged Stochastic Gra…