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09/06/2023
A Josephson Parametric Oscillator-Based Ising Machine
Ising machines have emerged as a promising solution for rapidly solving ...
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08/12/2023
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation
As the complexity and computational demands of deep learning models rise...
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07/14/2023
Brain Tumor Detection using Convolutional Neural Networks with Skip Connections
In this paper, we present different architectures of Convolutional Neura...
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07/07/2023
BlendNet: Design and Optimization of a Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions
This paper presents BlendNet, a neural network architecture employing a ...
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05/08/2023
SNT: Sharpness-Minimizing Network Transformation for Fast Compression-friendly Pretraining
Model compression has become the de-facto approach for optimizing the ef...
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04/13/2023
Algorithms and Hardware for Efficient Processing of Logic-based Neural Networks
Recent efforts to improve the performance of neural network (NN) acceler...
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04/11/2023
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
Secure computation is of critical importance to not only the DoD, but ac...
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03/30/2023
RPU: The Ring Processing Unit
Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many i...
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03/04/2023
A Fast Training-Free Compression Framework for Vision Transformers
Token pruning has emerged as an effective solution to speed up the infer...
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08/29/2022
AMR-MUL: An Approximate Maximally Redundant Signed Digit Multiplier
In this paper, we present an energy-efficient, yet high-speed approximat...
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08/17/2022
Better Than Worst-Case Decoding for Quantum Error Correction
The overheads of classical decoding for quantum error correction on supe...
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07/30/2022
Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis
Recent efforts for improving the performance of neural network (NN) acce...
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06/30/2022
Sparse Periodic Systolic Dataflow for Lowering Latency and Power Dissipation of Convolutional Neural Network Accelerators
This paper introduces the sparse periodic systolic (SPS) dataflow, which...
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04/07/2021
NullaNet Tiny: Ultra-low-latency DNN Inference Through Fixed-function Combinational Logic
While there is a large body of research on efficient processing of deep ...
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01/24/2021
A2P-MANN: Adaptive Attention Inference Hops Pruned Memory-Augmented Neural Networks
In this work, to limit the number of required attention inference hops i...
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01/07/2021
BRDS: An FPGA-based LSTM Accelerator with Row-Balanced Dual-Ratio Sparsification
In this paper, first, a hardware-friendly pruning algorithm for reducing...
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11/03/2020
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNs
This paper presents a dynamic network rewiring (DNR) method to generate ...
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07/30/2020
SynergicLearning: Neural Network-Based Feature Extraction for Highly-Accurate Hyperdimensional Learning
Machine learning models differ in terms of accuracy, computational/memor...
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07/03/2020
Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis
This paper aims at integrating three powerful techniques namely Deep Lea...
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02/13/2020
NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework
The shrinking of transistor geometries as well as the increasing complex...
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02/13/2020
CSM-NN: Current Source Model Based Logic Circuit Simulation – A Neural Network Approach
The miniaturization of transistors down to 5nm and beyond, plus the incr...
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02/12/2020
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space
Recent advances in the field of artificial intelligence have been made p...
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01/29/2020
qBSA: Logic Design of a 32-bit Block-Skewed RSFQ Arithmetic Logic Unit
Single flux quantum (SFQ) circuits are an attractive beyond-CMOS technol...
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01/29/2020
Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks
The high energy cost of processing deep convolutional neural networks im...
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01/14/2020
Run-time Deep Model Multiplexing
We propose a framework to design a light-weight neural multiplexer that ...
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12/11/2019
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning
Energy consumption is one of the most critical concerns in designing com...
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09/06/2019
Coarse2Fine: A Two-stage Training Method for Fine-grained Visual Classification
Small inter-class and large intra-class variations are the main challeng...
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05/11/2019
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework
Machine learning applied to architecture design presents a promising opp...
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05/10/2019
Energy-Aware Scheduling of Task Graphs with Imprecise Computations and End-to-End Deadlines
Imprecise computations provide an avenue for scheduling algorithms devel...
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02/04/2019
BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services
Recent studies have shown the latency and energy consumption of deep neu...
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02/01/2019
Hybrid Cell Assignment and Sizing for Power, Area, Delay Product Optimization of SRAM Arrays
Memory accounts for a considerable portion of the total power budget and...
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02/01/2019
Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach
Approximate Logic Synthesis (ALS) is the process of synthesizing and map...
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02/01/2019
Towards Collaborative Intelligence Friendly Architectures for Deep Learning
Modern mobile devices are equipped with high-performance hardware resour...
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12/30/2018
Space Expansion of Feature Selection for Designing more Accurate Error Predictors
Approximate computing is being considered as a promising design paradigm...
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12/19/2018
Modeling Processor Idle Times in MPSoC Platforms to Enable Integrated DPM, DVFS, and Task Scheduling Subject to a Hard Deadline
Energy efficiency is one of the most critical design criteria for modern...
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10/18/2018
Gradient Agreement as an Optimization Objective for Meta-Learning
This paper presents a novel optimization method for maximizing generaliz...
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09/21/2018
A Meta-Learning Approach for Custom Model Training
Transfer-learning and meta-learning are two effective methods to apply k...
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07/23/2018
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference
Deep neural networks have been successfully deployed in a wide variety o...
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06/03/2018
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications
Major advancements in building general-purpose and customized hardware h...
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02/02/2018
VIBNN: Hardware Acceleration of Bayesian Neural Networks
Bayesian Neural Networks (BNNs) have been proposed to address the proble...
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01/25/2018
JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services
Deep neural networks are among the most influential architectures of dee...
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01/11/2018
A Hardware-Friendly Algorithm for Scalable Training and Deployment of Dimensionality Reduction Models on FPGA
With ever-increasing application of machine learning models in various d...
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12/13/2017
FFT-Based Deep Learning Deployment in Embedded Systems
Deep learning has delivered its powerfulness in many application domains...
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07/06/2017