32 citations · 105 across the 20 of their papers we have counts for
20 papers
Long-Term and Short-Term Transistor Aging in Deep Neural Networks: Impact and Mitigation
Alireza Sarmadi, Virinchi Roy Surabhi, Prashanth Krishnamurthy +3
Deep neural networks (DNNs) are used in a variety of real-world applications including, for example, image classification and speech recognition. The inference accuracy of DNN impl…
First Demonstration of 28 nm Fabricated FeFET-Based Nonvolatile 6T SRAM
Albi Mema, Simon Thomann, Narendra Singh Dhakad +1
With the staggering increase of edge compute applications like Internet-of-Things (IoT) and artificial intelligence (AI), the demand for fast, energy-efficient on-chip memory is gr…
Self-Heating and Parasitic Effects in Multi-Tier CFET Design
Sufia Shahin, Mahdi Benkhelifa, Yogesh Singh Chauhan +1
In this article, we study the impact of self-heating effects (SHEs) and middle of line (MOL) and back-end of line (BEOL) induced parasitics on multi-tier CFET design, where multipl…
Carbon-Efficient 3D DNN Acceleration: Optimizing Performance and Sustainability
Aikaterini Maria Panteleaki, Konstantinos Balaskas, Georgios Zervakis +2
As Deep Neural Networks (DNNs) continue to drive advancements in artificial intelligence, the design of hardware accelerators faces growing concerns over embodied carbon footprint…
Kolmogorov-Arnold Network for Transistor Compact Modeling
Rodion Novkin, Hussam Amrouch
Neural network (NN)-based transistor compact modeling has recently emerged as a transformative solution for accelerating device modeling and SPICE circuit simulations. However, con…
Late Breaking Results: Leveraging Approximate Computing for Carbon-Aware DNN Accelerators
Aikaterini Maria Panteleaki, Konstantinos Balaskas, Georgios Zervakis +2
The rapid growth of Machine Learning (ML) has increased demand for DNN hardware accelerators, but their embodied carbon footprint poses significant environmental challenges. This p…
TReCiM: Lower Power and Temperature-Resilient Multibit 2FeFET-1T Compute-in-Memory Design
Yifei Zhou, Thomas Kämpfe, Kai Ni +3
Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the "…
Leveraging Highly Approximated Multipliers in DNN Inference
Georgios Zervakis, Fabio Frustaci, Ourania Spantidi +3
In this work, we present a control variate approximation technique that enables the exploitation of highly approximate multipliers in Deep Neural Network (DNN) accelerators. Our ap…
Energy-Efficient Cryogenic Ternary Content Addressable Memory using Ferroelectric SQUID
Shamiul Alam, Simon Thomann, Shivendra Singh Parihar +4
Ternary content addressable memories (TCAMs) are useful for certain computing tasks since they allow us to compare a search query with a whole dataset stored in the memory array. T…
Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET
Yifei Zhou, Xuchu Huang, Jianyi Yang +4
Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as 'memory wall' issue.…
HW/SW Codesign for Robust and Efficient Binarized SNNs by Capacitor Minimization
Mikail Yayla, Simon Thomann, Ming-Liang Wei +3
Using accelerators based on analog computing is an efficient way to process the immensely large workloads in Neural Networks (NNs). One example of an analog computing scheme for NN…
Powering Disturb-Free Reconfigurable Computing and Tunable Analog Electronics with Dual-Port Ferroelectric FET
Zijian Zhao, Shan Deng, Swetaki Chatterjee +15
Single-port ferroelectric FET (FeFET) that performs write and read operations on the same electrical gate prevents its wide application in tunable analog electronics and suffers fr…
Compact and High-Performance TCAM Based on Scaled Double-Gate FeFETs
Liu Liu, Shubham Kumar, Simon Thomann +3
Ternary content addressable memory (TCAM), widely used in network routers and high-associativity caches, is gaining popularity in machine learning and data-analytic applications. F…
AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks
Tim Bucher, Lilas Alrahis, Guilherme Paim +3
The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number of partners, while remarkably lengthening the supply chain. Thereby, security concerns,…
Positive/Negative Approximate Multipliers for DNN Accelerators
Ourania Spantidi, Georgios Zervakis, Iraklis Anagnostopoulos +2
Recent Deep Neural Networks (DNNs) managed to deliver superhuman accuracy levels on many AI tasks. Several applications rely more and more on DNNs to deliver sophisticated services…
Reliability-Aware Quantization for Anti-Aging NPUs
Sami Salamin, Georgios Zervakis, Ourania Spantidi +3
Transistor aging is one of the major concerns that challenges designers in advanced technologies. It profoundly degrades the reliability of circuits during its lifetime as it slows…
Control Variate Approximation for DNN Accelerators
Georgios Zervakis, Ourania Spantidi, Iraklis Anagnostopoulos +2
In this work, we introduce a control variate approximation technique for low error approximate Deep Neural Network (DNN) accelerators. The control variate technique is used in Mont…
Power Side-Channel Attacks in Negative Capacitance Transistor (NCFET)
Johann Knechtel, Satwik Patnaik, Mohammed Nabeel +5
Side-channel attacks have empowered bypassing of cryptographic components in circuits. Power side-channel (PSC) attacks have received particular traction, owing to their non-invasi…
Run-Time Accuracy Reconfigurable Stochastic Computing for Dynamic Reliability and Power Management
Shuyuan Yu, Han Zhou, Shaoyi Peng +3
In this paper, we propose a novel accuracy-reconfigurable stochastic computing (ARSC) framework for dynamic reliability and power management. Different than the existing stochastic…
SoftWear: Software-Only In-Memory Wear-Leveling for Non-Volatile Main Memory
Christian Hakert, Kuan-Hsun Chen, Paul R. Genssler +5
Several emerging technologies for byte-addressable non-volatile memory (NVM) have been considered to replace DRAM as the main memory in computer systems during the last years. The…