An overview of processing-in-memory circuits for artificial intelligence and machine learning
Artificial intelligence (AI) and machine learning (ML) are revolutionizing many fields of study,
such as visual recognition, natural language processing, autonomous vehicles, and …
such as visual recognition, natural language processing, autonomous vehicles, and …
A CMOS-integrated spintronic compute-in-memory macro for secure AI edge devices
YC Chiu, WS Khwa, CS Yang, SH Teng, HY Huang… - Nature …, 2023 - nature.com
Artificial intelligence edge devices should offer high inference accuracy and rapid response
times, as well as being energy efficient. Ensuring the security of these devices against …
times, as well as being energy efficient. Ensuring the security of these devices against …
Spinel ferrites for resistive random access memory applications
Cutting edge science and technology needs high quality data storage devices for their
applications in artificial intelligence and digital industries. Resistive random access memory …
applications in artificial intelligence and digital industries. Resistive random access memory …
A 40-nm MLC-RRAM compute-in-memory macro with sparsity control, on-chip write-verify, and temperature-independent ADC references
Resistive random access memory (RRAM)-based compute-in-memory (CIM) has shown
great potential for accelerating deep neural network (DNN) inference. However, device …
great potential for accelerating deep neural network (DNN) inference. However, device …
NeuroSim simulator for compute-in-memory hardware accelerator: Validation and benchmark
Compute-in-memory (CIM) is an attractive solution to process the extensive workloads of
multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware …
multiply-and-accumulate (MAC) operations in deep neural network (DNN) hardware …
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 …
A 40-nm, 64-kb, 56.67 TOPS/W voltage-sensing computing-in-memory/digital RRAM macro supporting iterative write with verification and online read-disturb detection
Computing-in-memory (CIM) architectures have gained importance in achieving high-
throughput energy-efficient artificial intelligence (AI) systems. Resistive RAM (RRAM) is a …
throughput energy-efficient artificial intelligence (AI) systems. Resistive RAM (RRAM) is a …
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 …
A 40-nm 118.44-TOPS/W voltage-sensing compute-in-memory RRAM macro with write verification and multi-bit encoding
Computing-in-memory (CIM) architectures have paved the way for energy-efficient artificial
intelligence (AI) systems while outperforming von Neumann architectures. In particular …
intelligence (AI) systems while outperforming von Neumann architectures. In particular …
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 …