Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.
[ACL'20] HAT: Hardware-Aware Transformers for Efficient Natural Language Processing
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)
Hardware-aware optimizer for placing edge AI inference stages across CPU, GPU, and NPU devices.
Hardware-aware Wave Function Collapse (WFC) scheduler for optimized AI workload mapping and resource constraints
Exploring constraint-aware neural network design through LUT-based FPGA pruning and RadioML signal classification analysis.
Treating the CPU cache hierarchy as a training-time constraint for faster native CPU LLM inference. Preprint + code.
Quantum Computing | Optimisation: Exploring hardware-aware variational quantum algorithms for optimisation and machine learning on near-term quantum devices.
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