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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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11/23/2022
End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture
The demand for computation resources and energy efficiency of Convolutio...
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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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06/03/2022
Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference
Analog In-Memory Computing (AIMC) is emerging as a disruptive paradigm f...
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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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01/04/2022
A Heterogeneous In-Memory Computing Cluster For Flexible End-to-End Inference of Real-World Deep Neural Networks
Deployment of modern TinyML tasks on small battery-constrained IoT devic...
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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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09/03/2021
End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We There Yet?
In-Memory Acceleration (IMA) promises major efficiency improvements in d...
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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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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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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