17 citations · 99 across the 66 of their papers we have counts for
13 papers · 1 filter
Low-Precision Mixed-Computation Models for Inference on Edge
Seyedarmin Azizi, Mahdi Nazemi, Mehdi Kamal +1
This paper presents a mixed-computation neural network processing approach for edge applications that incorporates low-precision (low-width) Posit and low-precision fixed point (Fi…
Superconductor Logic Implementation with All-JJ Inductor-Free Cell Library
Haolin Cong, Sasan Razmkhah, Mustafa Altay Karamuftuoglu +1
Single flux quantum (SFQ) technology has garnered significant attention due to its low switching power and high operational speed. Researchers have been actively pursuing more adva…
An On-Chip Trainable Neuron Circuit for SFQ-Based Spiking Neural Networks
Beyza Zeynep Ucpinar, Mustafa Altay Karamuftuoglu, Sasan Razmkhah +1
We present an on-chip trainable neuron circuit. Our proposed circuit suits bio-inspired spike-based time-dependent data computation for training spiking neural networks (SNN). The…
Unsupervised SFQ-Based Spiking Neural Network
Mustafa Altay Karamuftuoglu, Beyza Zeynep Ucpinar, Sasan Razmkhah +2
Single Flux Quantum (SFQ) technology represents a groundbreaking advancement in computational efficiency and ultra-high-speed neuromorphic processing. The key features of SFQ techn…
A Josephson Parametric Oscillator-Based Ising Machine
Sasan Razmkhah, Mehdi Kamal, Nobuyuki Yoshikawa +1
Ising machines have emerged as a promising solution for rapidly solving NP-complete combinatorial optimization problems, surpassing the capabilities of traditional computing method…
Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation
Seyedarmin Azizi, Mahdi Nazemi, Arash Fayyazi +1
As the complexity and computational demands of deep learning models rise, the need for effective optimization methods for neural network designs becomes paramount. This work introd…
Brain Tumor Detection using Convolutional Neural Networks with Skip Connections
Aupam Hamran, Marzieh Vaeztourshizi, Amirhossein Esmaili +1
In this paper, we present different architectures of Convolutional Neural Networks (CNN) to analyze and classify the brain tumors into benign and malignant types using the Magnetic…
NeuroBlend: Towards Low-Power yet Accurate Neural Network-Based Inference Engine Blending Binary and Fixed-Point Convolutions
Arash Fayyazi, Mahdi Nazemi, Arya Fayyazi +1
This paper introduces NeuroBlend, a novel neural network architecture featuring a unique building block known as the Blend module. This module incorporates binary and fixed-point c…
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation
Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi +1
Transfer learning has become a popular task adaptation method in the era of foundation models. However, many foundation models require large storage and computing resources, which…
TREBUCHET: Fully Homomorphic Encryption Accelerator for Deep Computation
David Bruce Cousins, Yuriy Polyakov, Ahmad Al Badawi +21
Secure computation is of critical importance to not only the DoD, but across financial institutions, healthcare, and anywhere personally identifiable information (PII) is accessed.…
Algorithms and Hardware for Efficient Processing of Logic-based Neural Networks
Jingkai Hong, Arash Fayyazi, Amirhossein Esmaili +2
Recent efforts to improve the performance of neural network (NN) accelerators that meet today's application requirements have given rise to a new trend of logic-based NN inference…
RPU: The Ring Processing Unit
Deepraj Soni, Negar Neda, Naifeng Zhang +14
Ring-Learning-with-Errors (RLWE) has emerged as the foundation of many important techniques for improving security and privacy, including homomorphic encryption and post-quantum cr…
Training-Free Acceleration of ViTs with Delayed Spatial Merging
Jung Hwan Heo, Seyedarmin Azizi, Arash Fayyazi +1
Token merging has emerged as a new paradigm that can accelerate the inference of Vision Transformers (ViTs) without any retraining or fine-tuning. To push the frontier of training-…