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Rapid learning with phase-change memory-based in-memory computing through learning-to-learn
There is a growing demand for low-power, autonomously learning artificial intelligence (AI)
systems that can be applied at the edge and rapidly adapt to the specific situation at …
systems that can be applied at the edge and rapidly adapt to the specific situation at …
The inherent adversarial robustness of analog in-memory computing
A key challenge for deep neural network algorithms is their vulnerability to adversarial
attacks. Inherently non-deterministic compute substrates, such as those based on analog in …
attacks. Inherently non-deterministic compute substrates, such as those based on analog in …
Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing
Large language models (LLMs), with their remarkable generative capacities, have greatly
impacted a range of fields, but they face scalability challenges due to their large parameter …
impacted a range of fields, but they face scalability challenges due to their large parameter …
Kernel approximation using analogue in-memory computing
Kernel functions are vital ingredients of several machine learning (ML) algorithms but often
incur substantial memory and computational costs. We introduce an approach to kernel …
incur substantial memory and computational costs. We introduce an approach to kernel …
Programming weights to analog in-memory computing cores by direct minimization of the matrix-vector multiplication error
Accurate programming of non-volatile memory (NVM) devices in analog in-memory
computing (AIMC) cores is critical to achieve high matrix-vector multiplication (MVM) …
computing (AIMC) cores is critical to achieve high matrix-vector multiplication (MVM) …
Improving the accuracy of analog-based in-memory computing accelerators post-training
Analog-Based In-Memory Computing (AIMC) inference accelerators can be used to
efficiently execute Deep Neural Network (DNN) inference workloads. However, to mitigate …
efficiently execute Deep Neural Network (DNN) inference workloads. However, to mitigate …
Demonstration of 4-quadrant analog in-memory matrix multiplication in a single modulation
Analog in-memory computing (AIMC) leverages the inherent physical characteristics of
resistive memory devices to execute computational operations, notably matrix-vector …
resistive memory devices to execute computational operations, notably matrix-vector …
Learning-to-learn enables rapid learning with phase-change memory-based in-memory computing
There is a growing demand for low-power, autonomously learning artificial intelligence (AI)
systems that can be applied at the edge and rapidly adapt to the specific situation at …
systems that can be applied at the edge and rapidly adapt to the specific situation at …
Opposing Mean Error Compensation for Accuracy Enhancement in Analog Compute-in-Memory With Resistive Switching Devices
Analog compute-in-memory (ACiM) systems show promise for energy-efficient AI inference,
but their performance is hindered by variations in conductance, resulting in reduced …
but their performance is hindered by variations in conductance, resulting in reduced …
Exploring learning techniques for edge AI taking advantage of non-volatile memories
M Martemucci - 2024 - theses.hal.science
Learning-capable edge artificial intelligence (AI) systems require both inference and
learning capabilities combined with energy efficiency. However, no existing memory …
learning capabilities combined with energy efficiency. However, no existing memory …