Memristive dynamics enabled neuromorphic computing systems
The slowing down of transistor scaling and explosive growth for intelligence computing
power emerge as the two driving factors for the study of novel devices and materials to …
power emerge as the two driving factors for the study of novel devices and materials to …
PICO-RAM: A PVT-Insensitive Analog Compute-In-Memory SRAM Macro With In Situ Multi-Bit Charge Computing and 6T Thin-Cell-Compatible Layout
Analog compute-in-memory (CIM) in static random access memory (SRAM) is promising for
accelerating deep learning inference by circumventing the memory wall and exploiting ultra …
accelerating deep learning inference by circumventing the memory wall and exploiting ultra …
A 161.6 tops/w mixed-mode computing-in-memory processor for energy-efficient mixed-precision deep neural networks
A Mixed-mode Computing-in memory (CIM) processor for the mixed-precision Deep Neural
Network (DNN) processing is proposed. Due to the bit-serial processing for the multi-bit …
Network (DNN) processing is proposed. Due to the bit-serial processing for the multi-bit …
A Quantization Model Based on a Floating-point Computing-in-Memory Architecture
Computing-in-memory (CIM) has been proved to perform high energy efficiency and
significant acceleration effect for high computational parallelism neural networks. Floating …
significant acceleration effect for high computational parallelism neural networks. Floating …
A 9T-SRAM in-memory computing macro for Boolean logic and multiply-and-accumulate operations
C Dai, Z Ren, L Guan, H Liu, M Gao, W Lu, Z Pang… - Microelectronics …, 2024 - Elsevier
Artificial intelligence algorithms play important roles in image classification to speech
recognition, which contains enormous Boolean logic and multiplication operations …
recognition, which contains enormous Boolean logic and multiplication operations …
Toggle Rate Aware Quantization Model Based on Digital Floating-Point Computing-in-Memory Architecture
X Chen, Y Zhao, A Guo, J Chen, F Dong… - … on Circuits and …, 2024 - ieeexplore.ieee.org
Computing-in-memory (CIM) has been proven to achieve high energy efficiency and
significant acceleration effects on neural networks with high computational parallelism …
significant acceleration effects on neural networks with high computational parallelism …
Design of processing-in-memory with triple computational path and sparsity handling for energy-efficient DNN training
As machine learning (ML) and artificial intelligence (AI) have become mainstream
technologies, many accelerators have been proposed to cope with their computation …
technologies, many accelerators have been proposed to cope with their computation …
Hadamard product-based in-memory computing design for floating point neural network training
Deep neural networks (DNNs) are one of the key fields of machine learning. It requires
considerable computational resources for cognitive tasks. As a novel technology to perform …
considerable computational resources for cognitive tasks. As a novel technology to perform …
A Multi-Level Deep Neural Network-Based Tourism Supply Chain Risk Management Study
L Xu - Scalable Computing: Practice and Experience, 2024 - scpe.org
With the rapid advancement of the tourism, the capital demand of tourism enterprises has
gradually risen, but the confusion of market management has increased the difficulty of risk …
gradually risen, but the confusion of market management has increased the difficulty of risk …
Mixed-Signal Non-Von Neumann Accelerators for Edge Computing
Z Chen - 2024 - search.proquest.com
Deep learning models deployed on edge devices for local inference offer superior latency,
efficiency, availability, scalability, and privacy over cloud-based inference. Due to the …
efficiency, availability, scalability, and privacy over cloud-based inference. Due to the …