Search Results for author:
Found 8 papers, 1 papers with code
High-quality Single-model Deep Video Compression with Frame-Conv3D and Multi-frame Differential Modulation
This paper proposes a deep video compression method to simultaneously encode multiple frames with Frame-Conv3D and differential modulation.
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface
Performing data-intensive tasks in the von Neumann architecture is challenging to achieve both high performance and power efficiency due to the memory wall bottleneck.
Block-Wise Dynamic-Precision Neural Network Training Acceleration via Online Quantization Sensitivity Analytics
Data quantization is an effective method to accelerate neural network training and reduce power consumption.
SEFormer: Structure Embedding Transformer for 3D Object Detection
Effectively preserving and encoding structure features from objects in irregular and sparse LiDAR points is a key challenge to 3D object detection on point cloud.
Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing with Ferroelectric FETs
Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications.
Emerging Technologies
Adaptive Pixel-wise Structured Sparse Network for Efficient CNNs
To accelerate deep CNN models, this paper proposes a novel spatially adaptive framework that can dynamically generate pixel-wise sparsity according to the input image.
ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression
For example, the model is first pruned on the cloud and then transferred from cloud to end by UDA.
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-art in weight pruning results.
