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Found 6 papers, 1 papers with code
AIPerf: Automated machine learning as an AI-HPC benchmark
The de facto HPC benchmark LINPACK can not reflect AI computing power and I/O performance without representative workload.
Brain-inspired global-local learning incorporated with neuromorphic computing
We demonstrate the advantages of this model in multiple different tasks, including few-shot learning, continual learning, and fault-tolerance learning in neuromorphic vision sensors.
FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture
In this paper, we propose a full system stack solution, composed of a reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and its software system including neural synthesizer, temporal-to-spatial mapper, and placement & routing.
Programmable Neural Network Trojan for Pre-Trained Feature Extractor
Existing studies have explored the outsourced training attack scenario and transfer learning attack scenario in some small datasets for specific domains, with limited numbers of fixed target classes.
TETRIS: TilE-matching the TRemendous Irregular Sparsity
Increasing the sparsity granularity can lead to better hardware utilization, but it will compromise the sparsity for maintaining accuracy.
Bridging the Gap Between Neural Networks and Neuromorphic Hardware with A Neural Network Compiler
Different from developing neural networks (NNs) for general-purpose processors, the development for NN chips usually faces with some hardware-specific restrictions, such as limited precision of network signals and parameters, constrained computation scale, and limited types of non-linear functions.
