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 …
Compute-in-memory technologies and architectures for deep learning workloads
The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …
Semantic memory–based dynamic neural network using memristive ternary CIM and CAM for 2D and 3D vision
The brain is dynamic, associative, and efficient. It reconfigures by associating the inputs with
past experiences, with fused memory and processing. In contrast, AI models are static …
past experiences, with fused memory and processing. In contrast, AI models are static …
Alpine: Analog in-memory acceleration with tight processor integration for deep learning
Analog in-memory computing (AIMC) cores offers significant performance and energy
benefits for neural network inference with respect to digital logic (eg, CPUs). AIMCs …
benefits for neural network inference with respect to digital logic (eg, CPUs). AIMCs …
Rapidx: High-performance reram processing in-memory accelerator for sequence alignment
Genome sequence alignment is the core of many biological applications. The advancement
of sequencing technologies produces a tremendous amount of data, making sequence …
of sequencing technologies produces a tremendous amount of data, making sequence …
34.9 A Flash-SRAM-ADC-Fused Plastic Computing-in-Memory Macro for Learning in Neural Networks in a Standard 14nm FinFET Process
L Wang, W Li, Z Zhou, H Gao, Z Li, W Ye… - … Solid-State Circuits …, 2024 - ieeexplore.ieee.org
AI edge devices are not only required to perform inference tasks with low power and high
real-time performance but are also expected to have the capability to learn and adapt to …
real-time performance but are also expected to have the capability to learn and adapt to …
Fusing in-storage and near-storage acceleration of convolutional neural networks
I Okafor, AK Ramanathan, NR Challapalle, Z Li… - ACM Journal on …, 2023 - dl.acm.org
Video analytics has a wide range of applications and has attracted much interest over the
years. While it can be both computationally and energy-intensive, video analytics can greatly …
years. While it can be both computationally and energy-intensive, video analytics can greatly …
Evidence of transport degradation in 22 nm FD-SOI charge trapping transistors for neural network applications
This article reports on the characterization and analysis of 22 nm FD-SOI CMOS technology-
based charge trap transistors (CTT) and their application in neural networks. The working …
based charge trap transistors (CTT) and their application in neural networks. The working …
Low‐Power Charge Trap Flash Memory with MoS2 Channel for High‐Density In‐Memory Computing
With the rise of on‐device artificial intelligence (AI) technology, the demand for in‐memory
comptuing has surged for data‐intensive tasks on edge devices. However, on‐device AI …
comptuing has surged for data‐intensive tasks on edge devices. However, on‐device AI …
Resonant Compute-In-Memory (rCIM) 10T SRAM Macro for Boolean Logic
D Challagundla, I Bezzam, B Saha… - 2023 IEEE 41st …, 2023 - ieeexplore.ieee.org
Traditional State-of-the-Art computing platforms have relied on silicon-based static random
access memories (SRAM) and digital Boolean logic for intensive computations. Although the …
access memories (SRAM) and digital Boolean logic for intensive computations. Although the …