15 citations · 26 across the 7 of their papers we have counts for
7 papers
Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation
Nathaniel Pinckney, Christopher Batten, Mingjie Liu +2
The application of large-language models (LLMs) to digital hardware code generation is an emerging field, with most LLMs primarily trained on natural language and software code. Ha…
Delving into Effective Gradient Matching for Dataset Condensation
Zixuan Jiang, Jiaqi Gu, Mingjie Liu +1
As deep learning models and datasets rapidly scale up, network training is extremely time-consuming and resource-costly. Instead of training on the entire dataset, learning with a…
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
Wei Shi, Hanrui Wang, Jiaqi Gu +4
Analog/mixed-signal circuit design is one of the most complex and time-consuming stages in the whole chip design process. Due to various process, voltage, and temperature (PVT) var…
ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement
Hanqing Zhu, Jiaqi Gu, Chenghao Feng +4
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with nea…
ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +5
Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. PTCs can achiev…
Towards Memory-Efficient Neural Networks via Multi-Level in situ Generation
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +4
Deep neural networks (DNN) have shown superior performance in a variety of tasks. As they rapidly evolve, their escalating computation and memory demands make it challenging to dep…
Optimizer Fusion: Efficient Training with Better Locality and Parallelism
Zixuan Jiang, Jiaqi Gu, Mingjie Liu +2
Machine learning frameworks adopt iterative optimizers to train neural networks. Conventional eager execution separates the updating of trainable parameters from forward and backwa…