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Optimizing event-based neural networks on digital neuromorphic architecture: A comprehensive design space exploration

Xu, Yingfu (author), Shidqi, Kevin (author), van Schaik, Gert-Jan (author), Bilgic, Refik (author), Dobrita, Alexandra (author), Wang, Shenqi (author), Gebregiorgis, A.B. (author), Hamdioui, S. (author), Yousefzadeh, Amirreza (author)

Neuromorphic processors promise low-latency and energy-efficient processing by adopting novel brain-inspired design methodologies. Yet, current neuromorphic solutions still struggle to rival conventional deep learning accelerators' performance and area efficiency in practical applications. Event-driven data-flow processing and near/in-memory...

journal article 2024

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Mapping-aware Biased Training for Accurate Memristor-based Neural Networks

Diware, S.S. (author), Gebregiorgis, A.B. (author), Joshi, R.V. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)

Memristor-based computation-in-memory (CIM) can achieve high energy efficiency by processing the data within the memory, which makes it well-suited for applications like neural networks. However, memristors suffer from conductance variation problem where their programmed conductance values deviate from the desired values. Such variations lead...

conference paper 2023

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Memristor-Based Lightweight Encryption

Siddiqi, M.A. (author), Hernández, Jan Andrés Galvan (author), Gebregiorgis, A.B. (author), Bishnoi, R.K. (author), Strydis, C. (author), Hamdioui, S. (author), Taouil, M. (author)

Next-generation personalized healthcare devices are undergoing extreme miniaturization in order to improve user acceptability. However, such developments make it difficult to incorporate cryptographic primitives using available target tech-nologies since these algorithms are notorious for their energy consumption. Besides, strengthening these...

conference paper 2023

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Energy-efficient Computation-In-Memory Architecture using Emerging Technologies

Bishnoi, R.K. (author), Diware, S.S. (author), Gebregiorgis, A.B. (author), Thomann, Simon (author), Mannaa, Sara (author), Deveautour, Bastien (author), Marchand, Cedric (author), Bosio, Alberto (author), Deleruyelle, Damien (author), O'Connor, Ian (author), Amrouch, Hussam (author), Hamdioui, S. (author)

Deep Learning (DL) has recently led to remark-able advancements, however, it faces severe computation related challenges. Existing Von-Neumann-based solutions are dealing with issues such as memory bandwidth limitations and energy inefficiency. Computation-In-Memory (CIM) has the potential to address this problem by integrating processing...

conference paper 2023

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PetaOps/W edge-AI μ Processors: Myth or reality?

Gomony, Manil Dev (author), de Putter, Floran (author), Gebregiorgis, A.B. (author), Paulin, Gianna (author), Mei, Linyan (author), Jain, Vikram (author), Hamdioui, S. (author), Bishnoi, R.K. (author), Sanchez, Victor (author)

With the rise of deep learning (DL), our world braces for artificial intelligence (AI) in every edge device, creating an urgent need for edge-AI SoCs. This SoC hardware needs to support high throughput, reliable and secure AI processing at ultra-low power (ULP), with a very short time to market. With its strong legacy in edge solutions and open...

conference paper 2023

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Dependability of Future Edge-AI Processors: Pandora’s Box

Gomony, Manil Dev (author), Gebregiorgis, A.B. (author), Fieback, M. (author), Geilen, Marc (author), Stuijk, Sander (author), Richter-Brockmann, Jan (author), Bishnoi, R.K. (author), Taouil, M. (author), Hamdioui, S. (author)

This paper addresses one of the directions of the HORIZON EU CONVOLVE project being dependability of smart edge processors based on computation-in-memory and emerging memristor devices such as RRAM. It discusses how how this alternative computing paradigm will change the way we used to do manufacturing test. In addition, it describes how these...

conference paper 2023

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A Survey on Machine Learning in Hardware Security

Köylü, T.C. (author), Reinbrecht, Cezar (author), Gebregiorgis, A.B. (author), Hamdioui, S. (author), Taouil, M. (author)

Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in other domains. This survey, as one of the early attempts, presents the usage of machine...

journal article 2023

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Accurate and Energy-Efficient Bit-Slicing for RRAM-Based Neural Networks

Diware, S.S. (author), Singh, A. (author), Gebregiorgis, A.B. (author), Joshi, Rajiv V. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)

Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as resistive random access memories (RRAMs) to process the data within the memory itself. This alleviates the memory-processor bottleneck resulting in much higher hardware efficiency compared to von-Neumann architecture-based conventional hardware. Hence, CIM...

journal article 2023

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Severity-based Hierarchical ECG Classification using Neural Networks

Diware, S.S. (author), Dash, Sudeshna (author), Gebregiorgis, A.B. (author), Joshi, Rajiv V. (author), Strydis, C. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)

Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to...

journal article 2023

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Analog monolayer SWCNTs-based memristive 2D structure for energy-efficient deep learning in spiking neural networks

Abunahla, H.N. (author), Abbas, Yawar (author), Gebregiorgis, A.B. (author), Waheed, Waqas (author), Mohammad, Baker (author), Hamdioui, S. (author), Alazzam, Anas (author), Rezeq, Moh’d (author)

Advances in materials science and memory devices work in tandem for the evolution of Artificial Intelligence systems. Energy-efficient computation is the ultimate goal of emerging memristor technology, in which the storage and computation can be done in the same memory crossbar. In this work, an analog memristor device is fabricated utilizing...

journal article 2023

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On the Reliability of RRAM-Based Neural Networks

Aziza, Hassen (author), Zambelli, Cristian (author), Hamdioui, S. (author), Diware, S.S. (author), Bishnoi, R.K. (author), Gebregiorgis, A.B. (author)

Emerging device technologies such as Resistive RAMs (RRAMs) are under investigation by many researchers and semiconductor companies; not only to realize e.g., embedded non-volatile memories, but also to enable energy-efficient computing making use of new data processing paradigms such as computation-in-memory. However, such devices suffer from...

conference paper 2023

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A Survey on Memory-centric Computer Architectures

Gebregiorgis, A.B. (author), Du Nguyen, H.A. (author), Yu, J. (author), Bishnoi, R.K. (author), Taouil, M. (author), Franky, Catthoor (author), Hamdioui, S. (author)

Faster and cheaper computers have been constantly demanding technological and architectural improvements. However, current technology is suffering from three technology walls: leakage wall, reliability wall, and cost wall. Meanwhile, existing architecture performance is also saturating due to three well-known architecture walls: memory wall,...

journal article 2022

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Hierarchical Memory Diagnosis

Cardoso Medeiros, G. (author), Fieback, M. (author), Gebregiorgis, A.B. (author), Taouil, M. (author), Poehls, L. B. (author), Hamdioui, S. (author)

High-quality memory diagnosis methodologies are critical enablers for scaled memory devices as they reduce time to market and provide valuable information regarding test escapes and customer returns. This paper presents an efficient Hierarchical Memory Diagnosis (HMD) approach that accurately diagnoses faults in the entire memory. Faults are...

conference paper 2022

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Accelerating RRAM Testing with a Low-cost Computation-in-Memory based DFT

Singh, A. (author), Fieback, M. (author), Bishnoi, R.K. (author), Bradarić, Filip (author), Gebregiorgis, A.B. (author), Joshi, R.V. (author), Hamdioui, S. (author)

Emerging non-volatile resistive RAM (RRAM) device technology has shown great potential to cultivate not only high-density memory storage, but also energy-efficient computing units. However, the unique challenges related to RRAM fabrication process render the traditional memory testing solutions inefficient and inadequate for high product quality...

conference paper 2022

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Energy-Efficient SNN Implementation Using RRAM-Based Computation In-Memory (CIM)

El Arrassi, A.E. (author), Gebregiorgis, A.B. (author), Haddadi, Anass El (author), Hamdioui, S. (author)

Spiking Neural Networks (SNNs) can drastically improve the energy efficiency of neuromorphic computing through network sparsity and event-driven execution. Thus, SNNs have the potential to support practical cognitive tasks on resource constrained platforms, such as edge devices. To realize this, SNN requires energy-efficient hardware which can...

conference paper 2022

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Dealing with Non-Idealities in Memristor Based Computation-In-Memory Designs

Gebregiorgis, A.B. (author), Singh, A. (author), Diware, S.S. (author), Bishnoi, R.K. (author), Hamdioui, S. (author)

Computation-In-Memory (CIM) using memristor devices provides an energy-efficient hardware implementation of arithmetic and logic operations for numerous applications, such as neuromorphic computing and database query. However, memristor-based CIM suffers from various non-idealities such as conductance drift, read disturb, wire parasitics,...

conference paper 2022

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PVT Analysis for RRAM and STT-MRAM-based Logic Computation-in-Memory

Fieback, M. (author), Münch, Christopher (author), Gebregiorgis, A.B. (author), Cardoso Medeiros, G. (author), Taouil, M. (author), Hamdioui, S. (author), Tahoori, Mehdi (author)

Emerging non-volatile resistive memories like Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM) and Resistive RAM (RRAM) are in the focus of today’s research. They offer promising alternative computing architectures such as computation-in-memory (CiM) to reduce the transfer overhead between CPU and memory, usually referred to as the...

conference paper 2022

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Energy-efficient In-Memory Address Calculation

Yousefzadeh, Amirreza (author), Stuijt, Jan (author), Hijdra, Martijn (author), Liu, Hsiao-Hsuan (author), Gebregiorgis, A.B. (author), Singh, A. (author), Hamdioui, S. (author), Catthoor, Francky (author)

Computation-in-Memory (CIM) is an emerging computing paradigm to address memory bottleneck challenges in computer architecture. A CIM unit cannot fully replace a general-purpose processor. Still, it significantly reduces the amount of data transfer between a traditional memory unit and the processor by enriching the transferred information....

journal article 2022

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CIM-based Robust Logic Accelerator using 28 nm STT-MRAM Characterization Chip Tape-out

Singh, A. (author), Zahedi, M.Z. (author), Shahroodi, Taha (author), Gupta, Mohit (author), Gebregiorgis, A.B. (author), Komalan, Manu (author), Joshi, R.V. (author), Catthoor, Francky (author), Bishnoi, R.K. (author), Hamdioui, S. (author)

Spin-transfer torque magnetic random access memory (STT-MRAM) based computation-in-memory (CIM) architectures have shown great prospects for an energy-efficient computing. However, device variations and non-idealities narrow down the sensing margin that severely impacts the computing accuracy. In this work, we propose an adaptive referencing...

conference paper 2022

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RRAM Crossbar-Based Fault-Tolerant Binary Neural Networks (BNNs)

Gebregiorgis, A.B. (author), Zografou, Artemis (author), Hamdioui, S. (author)

Computation-In Memory (CIM) using RRAM crossbar array is a promising solution to realize energy-efficient neuromorphic hardware, such as Binary Neural Networks (BNNs). However, RRAM faults restrict the applicability of CIM for BNN implementation. To address this issue, we propose a fault tolerance framework to mitigate the impact of RRAM faults...

conference paper 2022