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A Unified Learning Platform for Dynamic Frequency Scaling in Pipelined Processors
A machine learning (ML) design framework is proposed for dynamically adjusting clock frequency based on propagation delay of individual instructions.
Progressive VAE Training on Highly Sparse and Imbalanced Data
In each iteration, the additional layers are trained based on the intermediate pretrained model - a result of previous training iterations.
Exploiting Dual-Gate Ambipolar CNFETs for Scalable Machine Learning Classification
Ambipolar carbon nanotube based field-effect transistors (AP-CNFETs) exhibit unique electrical characteristics, such as tri-state operation and bi-directionality, enabling systems with complex and reconfigurable computing.
A Single-MOSFET MAC for Confidence and Resolution (CORE) Driven Machine Learning Classification
Mixed-signal machine-learning classification has recently been demonstrated as an efficient alternative for classification with power expensive digital circuits.
