Challenges and trends of SRAM-based computing-in-memory for AI edge devices
CJ Jhang, CX Xue, JM Hung… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
When applied to artificial intelligence edge devices, the conventionally von Neumann
computing architecture imposes numerous challenges (eg, improving the energy efficiency) …
computing architecture imposes numerous challenges (eg, improving the energy efficiency) …
Spiking neural network integrated circuits: A review of trends and future directions
The rapid growth of deep learning, spurred by its successes in various fields ranging from
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
Mixed-signal computing for deep neural network inference
B Murmann - IEEE Transactions on Very Large Scale …, 2020 - ieeexplore.ieee.org
Modern deep neural networks (DNNs) require billions of multiply-accumulate operations per
inference. Given that these computations demand relatively low precision, it is feasible to …
inference. Given that these computations demand relatively low precision, it is feasible to …
A design flow for mapping spiking neural networks to many-core neuromorphic hardware
The design of many-core neuromorphic hardware is becoming increasingly complex as
these systems are now expected to execute large machine-learning models. A predictable …
these systems are now expected to execute large machine-learning models. A predictable …
Hfnet: A cnn architecture co-designed for neuromorphic hardware with a crossbar array of synapses
The hardware-software co-optimization of neural network architectures is a field of research
that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth …
that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth …
Design-technology co-optimization for NVM-based neuromorphic processing elements
An emerging use case of machine learning (ML) is to train a model on a high-performance
system and deploy the trained model on energy-constrained embedded systems …
system and deploy the trained model on energy-constrained embedded systems …
Improving the Robustness of Neural Networks to Noisy Multi-Level Non-Volatile Memory-based Synapses
M Dampfhoffer, JM Lopez, T Mesquida… - … Joint Conference on …, 2023 - ieeexplore.ieee.org
The implementation of Artificial Neural Networks (ANNs) using analog Non-Volatile
Memories (NVMs) for synaptic weights storage promises improved energy-efficiency and …
Memories (NVMs) for synaptic weights storage promises improved energy-efficiency and …
Historical perspective and opportunity for computing in memory using floating-gate and resistive non-volatile computing including neuromorphic computing
JO Hasler, A Basu - Neuromorphic Computing and Engineering, 2025 - iopscience.iop.org
The effort addresses the research activity around the usage of Non-Volatile Memories (NVM)
for storage of “weights” in neural networks and the resulting computation through these …
for storage of “weights” in neural networks and the resulting computation through these …
Prospects for analog circuits in deep networks
Operations typically used in machine learning algorithms (eg, adds and soft max) can be
implemented by compact analog circuits. Analog Application-Specific Integrated Circuit …
implemented by compact analog circuits. Analog Application-Specific Integrated Circuit …
Models and algorithms for implementing energy-efficient spiking neural networks on neuromorphic hardware at the edge
M Dampfhoffer - 2023 - theses.hal.science
Deep learning in Artificial Neural Networks (ANNs), a branch of Artificial Intelligence (AI), is
considered a revolution in computing and is impacting every sectors of the economy …
considered a revolution in computing and is impacting every sectors of the economy …