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Found 32 papers, 17 papers with code
Characterizing Soft-Error Resiliency in Arm's Ethos-U55 Embedded Machine Learning Accelerator
As Neural Processing Units (NPU) or accelerators are increasingly deployed in a variety of applications including safety critical applications such as autonomous vehicle, and medical imaging, it is critical to understand the fault-tolerance nature of the NPUs.
Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions
Occlusion is a common problem with biometric recognition in the wild.
Autonomy 2.0: The Quest for Economies of Scale
With the advancement of robotics and AI technologies in the past decade, we have now entered the age of autonomous machines.
Thales: Formulating and Estimating Architectural Vulnerability Factors for DNN Accelerators
As Deep Neural Networks (DNNs) are increasingly deployed in safety critical and privacy sensitive applications such as autonomous driving and biometric authentication, it is critical to understand the fault-tolerance nature of DNNs.
ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization
In this work, we propose a fixed-length adaptive numerical data type called ANT to achieve low-bit quantization with tiny hardware overheads.
Perturbation Inactivation Based Adversarial Defense for Face Recognition
A straightforward approach is to inactivate the adversarial perturbations so that they can be easily handled as general perturbations.
SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation
This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements.
Block-Skim: Efficient Question Answering for Transformer
We further prune the hidden states corresponding to the unnecessary positions early in lower layers, achieving significant inference-time speedup.
Dataflow Accelerator Architecture for Autonomous Machine Computing
Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing.
S2TA: Exploiting Structured Sparsity for Energy-Efficient Mobile CNN Acceleration
We propose to exploit structured sparsity, more specifically, Density Bound Block (DBB) sparsity for both weights and activations.
One Shot Face Swapping on Megapixels
Extensive experiments demonstrate the superiority of MegaFS and the first megapixel level face swapping database is released for research on DeepFake detection and face image editing in the public domain.
Ranked #8 on
Face Swapping
on FaceForensics++
Fast and Accurate: Video Enhancement using Sparse Depth
This paper presents a general framework to build fast and accurate algorithms for video enhancement tasks such as super-resolution, deblurring, and denoising.
Block Skim Transformer for Efficient Question Answering
Following this idea, we proposed Block Skim Transformer (BST) to improve and accelerate the processing of transformer QA models.
Eudoxus: Characterizing and Accelerating Localization in Autonomous Machines
We develop and commercialize autonomous machines, such as logistic robots and self-driving cars, around the globe.
Self-Driving Cars
Hardware Architecture
End-to-End Framework for Efficient Deep Learning Using Metasurfaces Optics
Deep learning using Convolutional Neural Networks (CNNs) has been shown to significantly out-performed many conventional vision algorithms.
A Survey of FPGA-Based Robotic Computing
On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios.
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity
Network pruning can reduce the high computation cost of deep neural network (DNN) models.
Mesorasi: Architecture Support for Point Cloud Analytics via Delayed-Aggregation
Point cloud analytics is poised to become a key workload on battery-powered embedded and mobile platforms in a wide range of emerging application domains, such as autonomous driving, robotics, and augmented reality, where efficiency is paramount.
Balancing Efficiency and Flexibility for DNN Acceleration via Temporal GPU-Systolic Array Integration
We propose Simultaneous Multi-mode Architecture (SMA), a novel architecture design and execution model that offers general-purpose programmability on DNN accelerators in order to accelerate end-to-end applications.
Tigris: Architecture and Algorithms for 3D Perception in Point Clouds
While KD-tree search is inherently sequential, we propose an acceleration-amenable data structure and search algorithm that exposes different forms of parallelism of KD-tree search in the context of point cloud registration.
ASV: Accelerated Stereo Vision System
The key to ASV is to exploit unique characteristics inherent to stereo vision, and apply stereo-specific optimizations, both algorithmically and computationally.
Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
A key parameter that all existing compression techniques are sensitive to is the compression ratio (e. g., pruning sparsity, quantization bitwidth) of each layer.
Adversarial Defense Through Network Profiling Based Path Extraction
Recently, researchers have started decomposing deep neural network models according to their semantics or functions.
Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
In this paper, we propose a deep multi-task learning framework, named as IrisParseNet, to exploit the inherent correlations between pupil, iris and sclera to boost up the performance of iris segmentation and localization in a unified model.
Ranked #1 on
Iris Segmentation
on CASIA
ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model
The energy estimate model allows us to formulate DNN compression as a constrained optimization that minimizes the DNN loss function over the energy constraint.
Recognizing Partial Biometric Patterns
Biometric recognition on partial captured targets is challenging, where only several partial observations of objects are available for matching.
SCALE-Sim: Systolic CNN Accelerator
Systolic Arrays are one of the most popular compute substrates within Deep Learning accelerators today, as they provide extremely high efficiency for running dense matrix multiplications.
Distributed, Parallel, and Cluster Computing Hardware Architecture
Effective Path: Know the Unknowns of Neural Network
Despite their enormous success, there is still no solid understanding of deep neural network’s working mechanism.
Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
Deep Neural Networks (DNNs) are increasingly deployed in highly energy-constrained environments such as autonomous drones and wearable devices while at the same time must operate in real-time.
Euphrates: Algorithm-SoC Co-Design for Low-Power Mobile Continuous Vision
Specifically, we propose to expose the motion data that is naturally generated by the Image Signal Processor (ISP) early in the vision pipeline to the CNN engine.
Cloud No Longer a Silver Bullet, Edge to the Rescue
This paper takes the position that, while cognitive computing today relies heavily on the cloud, we will soon see a paradigm shift where cognitive computing primarily happens on network edges.
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective
Machine learning is playing an increasingly significant role in emerging mobile application domains such as AR/VR, ADAS, etc.
