Dr. Ji Lin
Senior Research Scientist
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Dr. Ji Lin. Every entry undergoes editorial source verification.
AWQ: Activation-aware Weight Quantization for On-Device LLM Compression
Authored the MLSys 2024 Best Paper introducing AWQ, demonstrating that protecting the top 1% salient weight channels based on activation magnitudes avoids backpropagation retraining and achieves lossless 4-bit inference.
Extreme 2-bit quantization still requires second-order error compensation beyond magnitude scaling.
MCUNet: Tiny Deep Learning on IoT Devices with TinyEngine
Engineered TinyEngine, a specialized neural network runtime that co-designs model architecture (TinyNAS) and memory scheduling to run deep learning vision and speech models on microcontrollers with only 256KB SRAM.
Static memory layout requires exact pre-compilation; dynamic tensor shapes are not supported on microcontrollers.