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Found 4 papers, 2 papers with code
An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators
Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML.
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
The modeling effort of SimDIT comprehensively covers convolution and non-convolution operations of both CNN inference and training on a highly parameterizable hardware substrate.
GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching
Graph neural networks (GNN) analysis engines are vital for real-world problems that use large graph models.
NeuPart: Using Analytical Models to Drive Energy-Efficient Partitioning of CNN Computations on Cloud-Connected Mobile Clients
This work optimizes energy on a mobile client by partitioning CNN computations between in situ processing on the client and offloaded computations in the cloud.
Distributed, Parallel, and Cluster Computing Signal Processing
