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07/18/2023
Using the IBM Analog In-Memory Hardware Acceleration Kit for Neural Network Training and Inference
Analog In-Memory Computing (AIMC) is a promising approach to reduce the ...
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05/26/2023
Gradient descent-based programming of analog in-memory computing cores
The precise programming of crossbar arrays of unit-cells is crucial for ...
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05/17/2023
AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing
The advancement of Deep Learning (DL) is driven by efficient Deep Neural...
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02/16/2023
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing (AIMC) – a promising approach for energy-effi...
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11/10/2021
AnalogNets: ML-HW Co-Design of Noise-robust TinyML Models and Always-On Analog Compute-in-Memory Accelerator
Always-on TinyML perception tasks in IoT applications require very high ...
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04/05/2021
A flexible and fast PyTorch toolkit for simulating training and inference on analog crossbar arrays
We introduce the IBM Analog Hardware Acceleration Kit, a new and first o...
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10/05/2020
Robust High-dimensional Memory-augmented Neural Networks
Traditional neural networks require enormous amounts of data to build th...
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03/25/2020
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Deep neural networks (DNNs) have surpassed human-level accuracy in a var...
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06/04/2019
In-memory hyperdimensional computing
Hyperdimensional computing (HDC) is an emerging computing framework that...
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05/28/2019
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that h...
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06/17/2017