A training-free algorithm to harness multiple GPUs to accelerate diffusion model inference without sacrificing image quality.

We enable LLMs to work on infinite-length texts without compromising efficiency and performance.

LongLoRA takes advantage of shifted sparse attention to greatly reduce the finetuning cost of long context LLMs.

This TinyML project aims to enable efficient AI computing on the edge by innovating model compression techniques as well as high-performance system design.

TorchSparse++ is a high-performance computing library for efficient 3D sparse convolution. It offers significant performance improvement over TorchSparse++ by overlapping computation with memory access. It also searches for the best execution strategy for sparse workloads within a large design space through auto-tuning.

This project introduce PockEngine: a tiny, sparse and efficient engine to enable fine-tuning on various edge devices. PockEngine supports sparse backpropagation: it prunes the backward graph and sparsely updates the model with measured memory saving and latency reduction while maintaining the model quality.