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08/14/2021
SIAM: Chiplet-based Scalable In-Memory Acceleration with Mesh for Deep Neural Networks
In-memory computing (IMC) on a monolithic chip for deep learning faces d...
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07/06/2021
Impact of On-Chip Interconnect on In-Memory Acceleration of Deep Neural Networks
With the widespread use of Deep Neural Networks (DNNs), machine learning...
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02/10/2021
Hybrid In-memory Computing Architecture for the Training of Deep Neural Networks
The cost involved in training deep neural networks (DNNs) on von-Neumann...
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03/10/2020
Benchmarking TinyML Systems: Challenges and Direction
Recent advancements in ultra-low-power machine learning (TinyML) hardwar...
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09/16/2019
High-Throughput In-Memory Computing for Binary Deep Neural Networks with Monolithically Integrated RRAM and 90nm CMOS
Deep learning hardware designs have been bottlenecked by conventional me...
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08/15/2019
Automatic Compiler Based FPGA Accelerator for CNN Training
Training of convolutional neural networks (CNNs)on embedded platforms to...
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02/27/2019
FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning
The computational demands of computer vision tasks based on state-of-the...
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05/23/2018
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Neuromorphic engineering (NE) encompasses a diverse range of approaches ...
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04/19/2018
Minimizing Area and Energy of Deep Learning Hardware Design Using Collective Low Precision and Structured Compression
Deep learning algorithms have shown tremendous success in many recogniti...
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09/19/2017
Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations
We present a new back propagation based training algorithm for discrete-...
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09/29/2016
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
Deep learning has significantly advanced the state of the art in artific...
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05/16/2016