Titlebook: In-Memory Computing Hardware Accelerators for Data-Intensive Applications; Baker Mohammad,Yasmin Halawani Book 2024 The Editor(s) (if appl

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Meriem Bettayeb,Yasmin Halawani,Muhammad Umair Khan,Baker Mohammad,Hani Salehment intervention and particularly the foreign/domestic divide. Second, I want to argue that, according to the methodological status government activity has in a given research, the issues of validity and of conceptualisation will have different bearings. I will then introduce each of the contributi

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Yasmin Halawani,Baker Mohammadment intervention and particularly the foreign/domestic divide. Second, I want to argue that, according to the methodological status government activity has in a given research, the issues of validity and of conceptualisation will have different bearings. I will then introduce each of the contributi

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SRAM-Based In-Memory Computing: Circuits, Functions, and Applications,mpatible with cutting-edge technology..This chapter delves into the evolution of SRAM-based IMC technology, examining its circuit design, functionality, and application levels. We also discuss commercial SRAM-based IMC’s limitations, constraints, and future prospects. Furthermore, we explore the use

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MRAM-Based In-Memory Computing,in order to facilitate processing capabilities have also been proposed, but require further technological improvements to be applicable. Despite the low resistance ratio between parallel and anti-parallel states, and due to the attractive properties of the STT memories, particularly in terms of ener

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In-Memory Computing Using Phase Change Memory,lizing PCM-based IMC designs to accelerate computations efficiently. The applications cover various areas and schemes, including Compressed Sensing, Spiking Neural Networks (SNNs), Convolutional Neural Networks (CNNs), and Hyper-Dimensional Computing (HDC).

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Memristor-Based In-Memory Computing,challenges related to IMC with memristor devices and crossbars on the device, circuit, and system levels are discussed. Furthermore, in order to demonstrate the potential of the memristor device, especially its analog memory characteristics, which are essential for many applications, a RRAM-based tr

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In-Memory Computing Using FLASH Memory,he NAND flash memory market was valued at USD 46.62 billion in 2020, and it is expected to reach USD 85.36 billion by 2026, registering a CAGR of 11.27% during the forecast period (2021–2026). The current macro trends of AI and machine learning, mobility, and connectivity are favorable to the NAND m

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