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Found 7 papers, 4 papers with code
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
Logic Shrinkage: Learned FPGA Netlist Sparsity for Efficient Neural Network Inference
To address these issues, we propose logic shrinkage, a fine-grained netlist pruning methodology enabling K to be automatically learned for every LUT in a neural network targeted for FPGA inference.
Enabling Binary Neural Network Training on the Edge
The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training.
LUTNet: Learning FPGA Configurations for Highly Efficient Neural Network Inference
Research has shown that deep neural networks contain significant redundancy, and thus that high classification accuracy can be achieved even when weights and activations are quantized down to binary values.
LUTNet: Rethinking Inference in FPGA Soft Logic
Research has shown that deep neural networks contain significant redundancy, and that high classification accuracies can be achieved even when weights and activations are quantised down to binary values.
Deep Neural Network Approximation for Custom Hardware: Where We've Been, Where We're Going
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks.
Machine Learning for Antimicrobial Resistance
Biological datasets amenable to applied machine learning are more available today than ever before, yet they lack adequate representation in the Data-for-Good community.
