Search Results for author:
Found 23 papers, 3 papers with code
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.
A Secure and Efficient Federated Learning Framework for NLP
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks.
CAP-RAM: A Charge-Domain In-Memory Computing 6T-SRAM for Accurate and Precision-Programmable CNN Inference
A compact, accurate, and bitwidth-programmable in-memory computing (IMC) static random-access memory (SRAM) macro, named CAP-RAM, is presented for energy-efficient convolutional neural network (CNN) inference.
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.
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression
Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.
ESMFL: Efficient and Secure Models for Federated Learning
Nowadays, Deep Neural Networks are widely applied to various domains.
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
An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices
Weight pruning has been widely acknowledged as a straightforward and effective method to eliminate redundancy in Deep Neural Networks (DNN), thereby achieving acceleration on various platforms.
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning
Weight pruning of DNNs is proposed, but existing schemes represent two extremes in the design space: non-structured pruning is fine-grained, accurate, but not hardware friendly; structured pruning is coarse-grained, hardware-efficient, but with higher accuracy loss.
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.
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks
As a further optimization, we propose a density-adaptive regular-block (DARB) pruning that outperforms prior structured pruning work with high pruning ratio and decoding efficiency.
Deep Compressed Pneumonia Detection for Low-Power Embedded Devices
Deep neural networks (DNNs) have been expanded into medical fields and triggered the revolution of some medical applications by extracting complex features and achieving high accuracy and performance, etc.
Learning Dynamic Context Augmentation for Global Entity Linking
Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space.
Ranked #5 on
Entity Disambiguation
on AIDA-CoNLL
An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM
Memristor-based weight pruning and weight quantization have been seperately investigated and proven effectiveness in reducing area and power consumption compared to the original DNN model.
Tiny but Accurate: A Pruned, Quantized and Optimized Memristor Crossbar Framework for Ultra Efficient DNN Implementation
To mitigate the challenges, the memristor crossbar array has emerged as an intrinsically suitable matrix computation and low-power acceleration framework for DNN applications.
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.
KCAT: A Knowledge-Constraint Typing Annotation Tool
Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision.
Toward Extremely Low Bit and Lossless Accuracy in DNNs with Progressive ADMM
Weight quantization is one of the most important techniques of Deep Neural Networks (DNNs) model compression method.
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.
Learning Topics using Semantic Locality
The topic modeling discovers the latent topic probability of the given text documents.
FFT-Based Deep Learning Deployment in Embedded Systems
The large model size of DNNs, while providing excellent accuracy, also burdens the embedded platforms with intensive computation and storage.
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system.
