Search Results for author:
Found 15 papers, 9 papers with code
Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications
Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI), including computer vision, natural language processing and speech recognition.
TorchSparse: Efficient Point Cloud Inference Engine
TorchSparse directly optimizes the two bottlenecks of sparse convolution: irregular computation and data movement.
Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning
Federated Learning is an emerging direction in distributed machine learning that en-ables jointly training a model without sharing the data.
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits
Extensively evaluated with 12 QML and VQE benchmarks on 14 quantum computers, QuantumNAS significantly outperforms baselines.
NAAS: Neural Accelerator Architecture Search
Data-driven, automatic design space exploration of neural accelerator architecture is desirable for specialization and productivity.
Hardware-Centric AutoML for Mixed-Precision Quantization
Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures.
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
Ranked #1 on
Robust 3D Semantic Segmentation
on SemanticKITTI-C
MCUNet: Tiny Deep Learning on IoT Devices
Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones.
Lite Transformer with Long-Short Range Attention
For language modeling, Lite Transformer achieves 1. 8 lower perplexity than the transformer at around 500M MACs.
Ranked #36 on
Machine Translation
on WMT2014 English-French
Distributed Training Across the World
Traditional synchronous distributed training is performed inside a cluster, since it requires high bandwidth and low latency network (e. g. 25Gb Ethernet or Infini-band).
Point-Voxel CNN for Efficient 3D Deep Learning
The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution.
Ranked #1 on
3D Semantic Segmentation
on S3DIS
Design Automation for Efficient Deep Learning Computing
Moreover, we shorten the design cycle by 200x than previous work, so that we can afford to design specialized neural network models for different hardware platforms.
HAQ: Hardware-Aware Automated Quantization with Mixed Precision
Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures.
Deep Gradient Compression Reduce the Communication Bandwidth For distributed Traning
Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure.
Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections.
