Posts by Collection




Open-source Hybrid Classical-Quantum Machine Learning Framework


We release TorchQuantum, an open-source PyTorch-centric hybrid classical-quantum machine learning framework. We support easy construction of parameterized quantum circuits in PyTorch; bach mode inference and training on CPU/GPU;dynamic computation graph for easy debugging; and easy deployment on real quantum devices such as IBMQ. We can support more than 26-qubit simulation with order-of-magnitude faster speed than PennyLane. The official website is at qmlsys.

PyTorch Training Utility


We release pyutils, a collection of pytorch model training utilities. We support general PyTorch training facilities and special computing operators used in pytorch-onn. Hierarchical configuration, various optimizers, lr_schedulers, efficient optical neurocomputing operators are supported.

Open-source PyTorch-centric Optical Neural Network Library


We release TorchONN, an open-source PyTorch-centric optical neural network library. We support fast and scalable development, training, and optimization of customized ONN layers and models. The tool runs on both CPU and GPU. CUDA-accelerated batched operators achieves 10-50X speedup over CPU-based matrix decomposition and unitary group parametrization.

GPU-Accelerated VLSI Placement


We release DREAMPlace 3.0, a Deep learning toolkit-enabled VLSI placement engine. DREAMPlace 3.0 supports region constraints with multi-electrostatic fields and enhanced gradient descent optimization with quadratic density penalty and adaptive entropy injection. With the analogy between nonlinear VLSI placement and deep learning training problem, this tool is developed with deep learning toolkit for flexibility and efficiency. The tool runs on both CPU and GPU. Over 30X speedup over the CPU implementation (RePlAce) is achieved in global placement and legalization on ISPD 2005 contest benchmarks with a Nvidia Tesla V100 GPU. DREAMPlace also integrates a GPU-accelerated detailed placer, ABCDPlace, which can achieve around 16X speedup on million-size benchmarks over the widely-adopted sequential placer NTUPlace3 on CPU.



Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.