Search Results for author:
Found 27 papers, 3 papers with code
Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers
This work proposes Quasar-ViT, a hardware-oriented quantization-aware architecture search framework for ViTs, to design efficient ViT models for hardware implementation while preserving the accuracy.
A Video Coding Method Based on Neural Network for CLIC2024
Meanwhile, the deep learning methods propose a convolutional neural network-based loop filter (CNNLF), which is turned on/off based on the rate-distortion optimization at the CTU and frame level.
SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model
Our method employs a supernet training paradigm that targets various model cost and resolution options using a weight-sharing method.
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
Recent works show that 8-bit or lower weight quantization is feasible with minimal impact on end-to-end task performance, while the activation is still not quantized.
SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices
Specifically, we investigate the randomized behavior of the AQFP devices and analyze the impact of crossbar size on current attenuation, subsequently formulating the current amplitude into the values suitable for use in BNN computation.
HeatViT: Hardware-Efficient Adaptive Token Pruning for Vision Transformers
While vision transformers (ViTs) have continuously achieved new milestones in the field of computer vision, their sophisticated network architectures with high computation and memory costs have impeded their deployment on resource-limited edge devices.
StereoVoxelNet: Real-Time Obstacle Detection Based on Occupancy Voxels from a Stereo Camera Using Deep Neural Networks
Obstacle detection is a safety-critical problem in robot navigation, where stereo matching is a popular vision-based approach.
Auto-ViT-Acc: An FPGA-Aware Automatic Acceleration Framework for Vision Transformer with Mixed-Scheme Quantization
Compared with state-of-the-art ViT quantization work (algorithmic approach only without hardware acceleration), our quantization achieves 0. 47% to 1. 36% higher Top-1 accuracy under the same bit-width.
FAIVConf: Face enhancement for AI-based Video Conference with Low Bit-rate
Recently, high-quality video conferencing with fewer transmission bits has become a very hot and challenging problem.
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
Our approach achieves comparable and better performance, when compared not only to existing quantization techniques with INT32 multiplication or floating-point arithmetic, but also to the full-precision counterparts, achieving state-of-the-art performance.
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices.
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Systematical evaluation on accuracy, training speed, and memory footprint are conducted, where the proposed MEST framework consistently outperforms representative SOTA works.
GRIM: A General, Real-Time Deep Learning Inference Framework for Mobile Devices based on Fine-Grained Structured Weight Sparsity
It necessitates the sparse model inference via weight pruning, i. e., DNN weight sparsity, and it is desirable to design a new DNN weight sparsity scheme that can facilitate real-time inference on mobile devices while preserving a high sparse model accuracy.
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
More importantly, our method does not require extra hardware cost compared to the traditional two-column mapping scheme.
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
With weights stored in the ReRAM crossbar cells as conductance, when the input vector is applied to word lines, the matrix-vector multiplication results can be generated as the current in bit lines.
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices
The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, especially on mobile platforms.
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed.
Efficient Transformer-based Large Scale Language Representations using Hardware-friendly Block Structured Pruning
Pre-trained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks.
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices
The vanilla sparsity removes whole kernel groups, while KGS sparsity is a more fine-grained structured sparsity that enjoys higher flexibility while exploiting full on-device parallelism.
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework
Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices.
RTMobile: Beyond Real-Time Mobile Acceleration of RNNs for Speech Recognition
Recurrent neural networks (RNNs) based automatic speech recognition has nowadays become prevalent on mobile devices such as smart phones.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency
Structured weight pruning is a representative model compression technique of DNNs for hardware efficiency and inference accelerations.
BLK-REW: A Unified Block-based DNN Pruning Framework using Reweighted Regularization Method
Accelerating DNN execution on various resource-limited computing platforms has been a long-standing problem.
A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation
Thus, the footprint and power consumption of SOT-MRAM PIM can be reduced, while increasing the overall system throughput at the meantime, making our proposed ADMM-based SOT-MRAM PIM more energy efficiency and suitable for embedded systems or IoT devices.
Non-Structured DNN Weight Pruning -- Is It Beneficial in Any Platform?
Based on the proposed comparison framework, with the same accuracy and quantization, the results show that non-structrued pruning is not competitive in terms of both storage and computation efficiency.
ResNet Can Be Pruned 60x: Introducing Network Purification and Unused Path Removal (P-RM) after Weight Pruning
The state-of-art DNN structures involve high computation and great demand for memory storage which pose intensive challenge on DNN framework resources.
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.
