Memristive technologies for data storage, computation, encryption, and radio-frequency communication
Memristive devices, which combine a resistor with memory functions such that voltage
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …
pulses can change their resistance (and hence their memory state) in a nonvolatile manner …
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference
Analogue in-memory computing (AIMC) with resistive memory devices could reduce the
latency and energy consumption of deep neural network inference tasks by directly …
latency and energy consumption of deep neural network inference tasks by directly …
Towards efficient in-memory computing hardware for quantized neural networks: state-of-the-art, open challenges and perspectives
O Krestinskaya, L Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The amount of data processed in the cloud, the development of Internet-of-Things (IoT)
applications, and growing data privacy concerns force the transition from cloud-based to …
applications, and growing data privacy concerns force the transition from cloud-based to …
33.4 A 28nm 2Mb STT-MRAM computing-in-memory macro with a refined bit-cell and 22.4-41.5 TOPS/W for AI inference
Emerging non-volatile memory-based computing-in-memory (CIM) is an excellent fit for
resource-constrained edge-AI devices [1–6]. MRAM-CIM macros for MAC operations, at …
resource-constrained edge-AI devices [1–6]. MRAM-CIM macros for MAC operations, at …
Comprehending in-memory computing trends via proper benchmarking
NR Shanbhag, SK Roy - 2022 IEEE Custom Integrated Circuits …, 2022 - ieeexplore.ieee.org
Since its inception in 2014 [1], the modern version of in-memory computing (IMC) has
become an active area of research in integrated circuit design globally for realizing artificial …
become an active area of research in integrated circuit design globally for realizing artificial …
Benchmarking in-memory computing architectures
NR Shanbhag, SK Roy - IEEE Open Journal of the Solid-State …, 2022 - ieeexplore.ieee.org
In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …
implement energy-efficient machine learning (ML) systems. However, today, the energy …
HD-CIM: Hybrid-device computing-in-memory structure based on MRAM and SRAM to reduce weight loading energy of neural networks
SRAM based computing-in-memory (SRAM-CIM) techniques have been widely studied for
neural networks (NNs) to solve the “Von Neumann bottleneck”. However, as the scale of the …
neural networks (NNs) to solve the “Von Neumann bottleneck”. However, as the scale of the …
Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell
Memristor-based neural networks provide an exceptional energy-efficient platform for
artificial intelligence (AI), presenting the possibility of self-powered operation when paired …
artificial intelligence (AI), presenting the possibility of self-powered operation when paired …
A survey of MRAM-centric computing: From near memory to in memory
Conventional von Neumann architecture suffers from bottlenecks in computing performance
and power consumption due to frequent data exchange between memory and processing …
and power consumption due to frequent data exchange between memory and processing …
Adapting magnetoresistive memory devices for accurate and on-chip-training-free in-memory computing
Memristors have emerged as promising devices for enabling efficient multiply-accumulate
(MAC) operations in crossbar arrays, crucial for analog in-memory computing (AiMC) …
(MAC) operations in crossbar arrays, crucial for analog in-memory computing (AiMC) …