Searched for: subject%3A%22Computation%255C-in%255C-memory%22
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Singh, A. (author)
Conventional computing systems involve physically separated storing and processing units. To perform the processing, data is shuttled from the storing unit to the processing unit followed by the actual processing, and the processed data is shuttled back into the storing unit. Unfortunately, this data shuffling contributes significantly to the...
doctoral thesis 2024
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Shahroodi, T. (author)
Modern applications like Genomics and Machine Learning (ML) hold the potential to reshape our understanding of diseases’ genetic origins and guide machines in executing tasks and making predictions without our explicit programming. The successful, widespread integration of these modern applications can usher in advancements in di-agnostics,...
doctoral thesis 2024
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Diware, S.S. (author), Chilakala, Koteswararao (author), Joshi, Rajiv V. (author), Hamdioui, S. (author), Bishnoi, R.K. (author)
Diabetic retinopathy (DR) is a leading cause of permanent vision loss worldwide. It refers to irreversible retinal damage caused due to elevated glucose levels and blood pressure. Regular screening for DR can facilitate its early detection and timely treatment. Neural network-based DR classifiers can be leveraged to achieve such screening in...
journal article 2024
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Zahedi, M.Z. (author)
Computation-in-Memory (CIM) is a promising alternative to traditional computing systems where the storage is conceptually separated fromthe computing units. Instead, the CIM paradigm aims to perform the computation where the data resides, alleviating the memory bottleneck and ultimately leading to higher energy efficiency and performance. From...
doctoral thesis 2023
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Huijbregts, Lucas (author)
Ultra-low power Edge AI hardware is in increasing demand due to the battery-limited energy budget of typical Edge devices such as smartphones, wearables, and IoT sensor systems. For this purpose, this Thesis introduces an ultra-low power event-driven SRAM-based Compute In-Memory (CIM) accelerator optimized for inference of Binary Spiking Neural...
master thesis 2023
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Singh, A. (author), Bishnoi, R.K. (author), Kaichouhi, A. (author), Diware, S.S. (author), Joshi, R.V. (author), Hamdioui, S. (author)
Analog computation-in-memory (CIM) architecture alleviates massive data movement between the memory and the processor, thus promising great prospects to accelerate certain computational tasks in an energy-efficient manner. However, data converters involved in these architectures typically achieve the required computing accuracy at the expense...
conference paper 2023
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Shahroodi, Taha (author), Miao, Michael (author), Zahedi, M.Z. (author), Wong, J.S.S.M. (author), Hamdioui, S. (author)
The high execution time of DNA sequence alignment negatively affects many genomic studies that rely on sequence alignment results. Pre-alignment filtering was introduced as a step before alignment to reduce the execution time of short-read sequence alignment greatly. With its success, i.e., achieving high accuracy and thus removing...
conference paper 2023
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Escuin, Carlos (author), García-Redondo, Fernando (author), Zahedi, M.Z. (author), Ibáñez, Pablo (author), Monreal, Teresa (author), Viñals, Victor (author), Llabería, José María (author), Myers, James (author), Ryckaert, Julien (author), Biswas, Dwaipayan (author), Catthoor, Francky (author)
This paper optimizes the MNEMOSENE architecture, a compute-in-memory (CiM) tile design integrating computation and storage for increased efficiency. We identify and address bottlenecks in the Row Data (RD) buffer that cause losses in performance. Our proposed approach includes mitigating these buffering bottlenecks and extending MNEMOSENE’s...
conference paper 2023
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Shahroodi, Taha (author), Cardoso, Rafaela (author), Zahedi, M.Z. (author), Wong, J.S.S.M. (author), Bosio, Alberto (author), O'Connor, Ian (author), Hamdioui, S. (author)
This paper investigates the potential of a compute-in-memory core based on optical Phase Change Materials (oPCMs) to speed up and reduce the energy consumption of the Matrix-Matrix-Multiplication operation. The paper also proposes a new data mapping for Binary Neural Networks (BNNs) tailored for our oPCM core. The preliminary results show a...
conference paper 2023
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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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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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Zahedi, M.Z. (author), Shahroodi, Taha (author), Wong, J.S.S.M. (author), Hamdioui, S. (author)
The vast potential of memristor-based computation-in-memory (CIM) engines has mainly triggered the mapping of best-suited applications. Nevertheless, with additional support, existing applications can also benefit from CIM. In particular, this paper proposes an energy and area-efficient CIM-based methodology to perform arithmetic signed...
journal article 2023
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Fieback, M. (author)
Resistive random access memory (RRAM) is a promising emerging memory technology that offers dense, non-volatile memories that do not consume any static power. Furthermore, RRAMdevices can be written and read out in nanoseconds, and it is possible to use them to performcomputation-in-memory (CIM). These benefits make this technology a potential...
doctoral thesis 2022
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Sudhakar, Varun (author)
The ever-increasing energy demands of traditional computing platforms (CPU, GPU) for large-scale deployment of Artificial Intelligence (AI) has spawned an exploration for better alternatives to existing von-Neumann compute architectures. Computation In-Memory (CIM) using emerging memory technologies such as Resistive Random Access Memory (RRAM)...
master thesis 2022
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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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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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Zahedi, M.Z. (author), Abu Lebdeh, M.F.M. (author), Bengel, Christopher (author), Wouters, Dirk (author), Menzel, Stephan (author), Le Gallo, Manuel (author), Sebastian, Abu (author), Wong, J.S.S.M. (author), Hamdioui, S. (author)
In recent years, we are witnessing a trend toward in-memory computing for future generations of computers that differs from traditional von-Neumann architecture in which there is a clear distinction between computing and memory units. Considering that data movements between the central processing unit (CPU) and memory consume several orders...
journal article 2022
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Fieback, M. (author), Taouil, M. (author), Hamdioui, S. (author)
Testing of Computation-in-Memory (CIM) designs based on emerging non-volatile memory technologies, such as resistive RAM (RRAM), is fundamentally different from testing traditional memories. Such designs allow not only for data storage (i.e., memory configuration) but also for the execution of logical and arithmetic operations (i.e., computing...
conference paper 2022
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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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Chilakala, Koteswararao (author)
Diabetic Retinopathy (DR) is one of the leading causes of permanent vision loss. Its current prevalence is around 45 millions across the globe and is projected to 70 million by 2045. Most of the people with this disease condition belong to remote and low income settings. We can reduce this incidence, if quality medical care is accessible in...
master thesis 2021
Searched for: subject%3A%22Computation%255C-in%255C-memory%22
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