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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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03/24/2023
Factorizers for Distributed Sparse Block Codes
Distributed sparse block codes (SBCs) exhibit compact representations fo...
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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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02/04/2023
WHYPE: A Scale-Out Architecture with Wireless Over-the-Air Majority for Scalable In-memory Hyperdimensional Computing
Hyperdimensional computing (HDC) is an emerging computing paradigm that ...
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11/09/2022
In-memory factorization of holographic perceptual representations
Disentanglement of constituent factors of a sensory signal is central to...
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09/21/2022
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics
The massive use of artificial neural networks (ANNs), increasingly popul...
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07/14/2022
In-memory Realization of In-situ Few-shot Continual Learning with a Dynamically Evolving Explicit Memory
Continually learning new classes from a few training examples without fo...
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05/22/2022
Wireless On-Chip Communications for Scalable In-memory Hyperdimensional Computing
Hyperdimensional computing (HDC) is an emerging computing paradigm that ...
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05/20/2022
ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning
Analog in-memory computing (AIMC) cores offers significant performance a...
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03/30/2022
Constrained Few-shot Class-incremental Learning
Continually learning new classes from fresh data without forgetting prev...
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03/11/2022
Generalized Key-Value Memory to Flexibly Adjust Redundancy in Memory-Augmented Networks
Memory-augmented neural networks enhance a neural network with an extern...
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03/09/2022
A Neuro-vector-symbolic Architecture for Solving Raven's Progressive Matrices
Neither deep neural networks nor symbolic AI alone have approached the k...
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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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11/08/2020
Graphene-based Wireless Agile Interconnects for Massive Heterogeneous Multi-chip Processors
The main design principles in computer architecture have recently shifte...
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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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09/15/2020
Short-term synaptic plasticity optimally models continuous environments
Biological neural networks operate with extraordinary energy efficiency,...
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04/30/2020
Memristors – from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven ...
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04/08/2020
File Classification Based on Spiking Neural Networks
In this paper, we propose a system for file classification in large data...
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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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03/05/2020
Compiling Neural Networks for a Computational Memory Accelerator
Computational memory (CM) is a promising approach for accelerating infer...
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06/08/2019
5 Parallel Prism: A topology for pipelined implementations of convolutional neural networks using computational memory
In-memory computing is an emerging computing paradigm that could enable ...
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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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01/11/2019
Low-Power Neuromorphic Hardware for Signal Processing Applications
Machine learning has emerged as the dominant tool for implementing compl...
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06/17/2017
