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An Efficient Training Framework for Reversible Neural Architectures
In this work, we formulate the decision problem for reversible operators with training time as the objective function and memory usage as the constraint.
Practical Layout-Aware Analog/Mixed-Signal Design Automation with Bayesian Neural Networks
Our efficient algorithm solves the post-layout performance optimization problem where simulations are known to be expensive.
TAG: Learning Circuit Spatial Embedding From Layouts
Analog and mixed-signal (AMS) circuit designs still rely on human design expertise.
Optimizer Fusion: Efficient Training with Better Locality and Parallelism
Machine learning frameworks adopt iterative optimizers to train neural networks.
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