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07/18/2023
Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey
With an increasing number of smart devices like internet of things (IoT)...
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05/26/2023
Aggregating Capacity in FL through Successive Layer Training for Computationally-Constrained Devices
Federated learning (FL) is usually performed on resource-constrained edg...
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03/14/2023
Model-to-Circuit Cross-Approximation For Printed Machine Learning Classifiers
Printed electronics (PE) promises on-demand fabrication, low non-recurri...
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02/28/2023
Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits
Printed Electronics (PE) exhibits on-demand, extremely low-cost hardware...
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01/26/2023
Hardware-Aware Automated Neural Minimization for Printed Multilayer Perceptrons
The demand of many application domains for flexibility, stretchability, ...
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10/02/2022
Approximate Computing and the Efficient Machine Learning Expedition
Approximate computing (AxC) has been long accepted as a design alternati...
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07/25/2022
Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration
Deep Neural Networks (DNNs) are being heavily utilized in modern applica...
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06/11/2022
NPU-Accelerated Imitation Learning for Thermal Optimization of QoS-Constrained Heterogeneous Multi-Cores
Application migration and dynamic voltage and frequency scaling (DVFS) a...
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04/13/2022
Agent-based Constraint Solving for Resource Allocation in Manycore Systems
For efficiency reasons, manycore systems are increasingly heterogeneous,...
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03/16/2022
Hardware Approximate Techniques for Deep Neural Network Accelerators: A Survey
Deep Neural Networks (DNNs) are very popular because of their high perfo...
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03/11/2022
Cross-Layer Approximation For Printed Machine Learning Circuits
Printed electronics (PE) feature low non-recurring engineering costs and...
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03/10/2022
CoCo-FL: Communication- and Computation-Aware Federated Learning via Partial NN Freezing and Quantization
Devices participating in federated learning (FL) typically have heteroge...
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03/08/2022
AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch
Current state-of-the-art employs approximate multipliers to address the ...
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12/16/2021
DISTREAL: Distributed Resource-Aware Learning in Heterogeneous Systems
We study the problem of distributed training of neural networks (NNs) on...
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09/25/2021
CoMeT: An Integrated Interval Thermal Simulation Toolchain for 2D, 2.5D, and 3D Processor-Memory Systems
Processing cores and the accompanying main memory working in tandem enab...
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07/20/2021
Positive/Negative Approximate Multipliers for DNN Accelerators
Recent Deep Neural Networks (DNNs) managed to deliver superhuman accurac...
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03/08/2021
Reliability-Aware Quantization for Anti-Aging NPUs
Transistor aging is one of the major concerns that challenges designers ...
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02/18/2021
Control Variate Approximation for DNN Accelerators
In this work, we introduce a control variate approximation technique for...
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07/08/2020
Power Side-Channel Attacks in Negative Capacitance Transistor (NCFET)
Side-channel attacks have empowered bypassing of cryptographic component...
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06/09/2020
Distributed Learning on Heterogeneous Resource-Constrained Devices
We consider a distributed system, consisting of a heterogeneous set of d...
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04/07/2020
SoftWear: Software-Only In-Memory Wear-Leveling for Non-Volatile Main Memory
Several emerging technologies for byte-addressable non-volatile memory (...
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04/06/2020