research
∙
08/11/2023
INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing
An increasing number of researchers are finding use for nth-order gradie...
share
research
∙
06/29/2023
Rapid-INR: Storage Efficient CPU-free DNN Training Using Implicit Neural Representation
Implicit Neural Representation (INR) is an innovative approach for repre...
research
∙
05/30/2023
Edge-MoE: Memory-Efficient Multi-Task Vision Transformer Architecture with Task-level Sparsity via Mixture-of-Experts
Computer vision researchers are embracing two promising paradigms: Visio...
research
∙
04/21/2023
LightningSim: Fast and Accurate Trace-Based Simulation for High-Level Synthesis
High-Level Synthesis allows hardware designers to create complex RTL des...
research
∙
04/13/2023
DGNN-Booster: A Generic FPGA Accelerator Framework For Dynamic Graph Neural Network Inference
Dynamic Graph Neural Networks (DGNNs) are becoming increasingly popular ...
research
∙
03/29/2023
GNNBuilder: An Automated Framework for Generic Graph Neural Network Accelerator Generation, Simulation, and Optimization
There are plenty of graph neural network (GNN) accelerators being propos...
research
∙
03/14/2023
Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks
Reasoning high-level abstractions from bit-blasted Boolean networks (BNs...
research
∙
10/26/2022
M^3ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
Multi-task learning (MTL) encapsulates multiple learned tasks in a singl...
research
∙
10/16/2022
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices
In this paper, we propose a data-model-hardware tri-design framework for...
research
∙
10/08/2022
Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU
Dynamic graph neural network (DGNN) is becoming increasingly popular bec...
research
∙
07/13/2022
Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation
Using machine learning to solve combinatorial optimization (CO) problems...
research
∙
06/08/2022
RT-DNAS: Real-time Constrained Differentiable Neural Architecture Search for 3D Cardiac Cine MRI Segmentation
Accurately segmenting temporal frames of cine magnetic resonance imaging...
research
∙
04/29/2022
H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness
The complex nature of real-world problems calls for heterogeneity in bot...
research
∙
04/27/2022
FlowGNN: A Dataflow Architecture for Universal Graph Neural Network Inference via Multi-Queue Streaming
Graph neural networks (GNNs) have recently exploded in popularity thanks...
research
∙
01/21/2022
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Recently, numerous sparse hardware accelerators for Deep Neural Networks...
research
∙
01/20/2022
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration
Graph neural networks (GNNs) have recently exploded in popularity thanks...
research
∙
01/20/2022
Hybrid Graph Models for Logic Optimization via Spatio-Temporal Information
Despite the stride made by machine learning (ML) based performance model...
research
∙
01/18/2022
High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing
Agile hardware development requires fast and accurate circuit quality ev...
research
∙
09/13/2021
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation
Circuit design is complicated and requires extensive domain-specific exp...
research
∙
08/04/2021
Generic Neural Architecture Search via Regression
Most existing neural architecture search (NAS) algorithms are dedicated ...
research
∙
07/24/2021
ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation
High-level synthesis (HLS) has been widely adopted as it significantly i...
research
∙
07/09/2021
WinoCNN: Kernel Sharing Winograd Systolic Array for Efficient Convolutional Neural Network Acceleration on FPGAs
The combination of Winograd's algorithm and systolic array architecture ...
research
∙
06/10/2021
Adversarial Graph Augmentation to Improve Graph Contrastive Learning
Self-supervised learning of graph neural networks (GNN) is in great need...
research
∙
05/11/2021
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration
The deep neural network (DNN) based AI applications on the edge require ...
research
∙
04/13/2021
MELOPPR: Software/Hardware Co-design for Memory-efficient Low-latency Personalized PageRank
Personalized PageRank (PPR) is a graph algorithm that evaluates the impo...
research
∙
04/08/2021
Software/Hardware Co-design for Multi-modal Multi-task Learning in Autonomous Systems
Optimizing the quality of result (QoR) and the quality of service (QoS) ...
research
∙
04/07/2021
On-FPGA Training with Ultra Memory Reduction: A Low-Precision Tensor Method
Various hardware accelerators have been developed for energy-efficient a...
research
∙
03/25/2021
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Artificial intelligence (AI) technologies have dramatically advanced in ...
research
∙
02/16/2021
IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning
Despite the great success of High-Level Synthesis (HLS) tools, we observ...
research
∙
10/14/2020
Effective Algorithm-Accelerator Co-design for AI Solutions on Edge Devices
High quality AI solutions require joint optimization of AI algorithms, s...
research
∙
05/18/2020
VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization
Quantization has been proven to be an effective method for reducing the ...
research
∙
05/06/2020
EDD: Efficient Differentiable DNN Architecture and Implementation Co-search for Embedded AI Solutions
High quality AI solutions require joint optimization of AI algorithms an...
research
∙
01/06/2020
AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growin...
research
∙
11/18/2019
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving
The rapidly growing demands for powerful AI algorithms in many applicati...
research
∙
09/20/2019
SkyNet: a Hardware-Efficient Method for Object Detection and Tracking on Embedded Systems
Developing object detection and tracking on resource-constrained embedde...
research
∙
06/25/2019
SkyNet: A Champion Model for DAC-SDC on Low Power Object Detection
Developing artificial intelligence (AI) at the edge is always challengin...
research
∙
05/20/2019
A Bi-Directional Co-Design Approach to Enable Deep Learning on IoT Devices
Developing deep learning models for resource-constrained Internet-of-Thi...
research
∙
04/09/2019