Search Results - "Grimaldi, Matteo"

Optimality Assessment of Memory-Bounded ConvNets Deployed on Resource-Constrained RISC Cores by Grimaldi, Matteo, Peluso, Valentino, Calimera, Andrea

“…A cost-effective implementation of Convolutional Neural Nets on the mobile edge of the Internet-of-Things (IoT) requires smart optimizations to fit large…”
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Enabling Stroke Rehabilitation in Home and Community Settings: A Wearable Sensor-Based Approach for Upper-Limb Motor Training by Lee, Sunghoon I., Adans-Dester, Catherine P., Grimaldi, Matteo, Dowling, Ariel V., Horak, Peter C., Black-Schaffer, Randie M., Bonato, Paolo, Gwin, Joseph T.

“…High-dosage motor practice can significantly contribute to achieving functional recovery after a stroke. Performing rehabilitation exercises at home and using,…”
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Dynamic ConvNets on Tiny Devices via Nested Sparsity by Grimaldi, Matteo, Mocerino, Luca, Cipolletta, Antonio, Calimera, Andrea

“…This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets)…”
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Layer-Wise Compressive Training for Convolutional Neural Networks by Grimaldi, Matteo, Tenace, Valerio, Calimera, Andrea

“…Convolutional Neural Networks (CNNs) are brain-inspired computational models designed to recognize patterns. Recent advances demonstrate that CNNs are able to…”
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Muscle Synergies as the Basis for the Control of a Hand Prosthesis by Bonato, Paolo, Sapienza, Stefano, Grimaldi, Matteo, Fabara, Eric, Daneault, Jean-Francois

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Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity by Grimaldi, Matteo, Ganji, Darshan C, Lazarevich, Ivan, Sah, Sudhakar

“…The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment. Exploiting…”
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MCUBench: A Benchmark of Tiny Object Detectors on MCUs by Sah, Sudhakar, Ganji, Darshan C, Grimaldi, Matteo, Kumar, Ravish, Hoffman, Alexander, Rohmetra, Honnesh, Saboori, Ehsan

“…We introduce MCUBench, a benchmark featuring over 100 YOLO-based object detection models evaluated on the VOC dataset across seven different MCUs. This…”
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YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems by Lazarevich, Ivan, Grimaldi, Matteo, Kumar, Ravish, Mitra, Saptarshi, Khan, Shahrukh, Sah, Sudhakar

“…We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86…”
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EAST: Encoding-Aware Sparse Training for Deep Memory Compression of ConvNets by Grimaldi, Matteo, Peluso, Valentino, Calimera, Andrea

“…The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression…”
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Dynamic ConvNets on Tiny Devices via Nested Sparsity by Grimaldi, Matteo, Mocerino, Luca, Cipolletta, Antonio, Calimera, Andrea

“…This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets)…”
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YOLOBench: Benchmarking Efficient Object Detectors on Embedded Systems by Lazarevich, Ivan, Grimaldi, Matteo, Kumar, Ravish, Mitra, Saptarshi, Khan, Shahrukh, Sah, Sudhakar

“…We present YOLOBench, a benchmark comprised of 550+ YOLO-based object detection models on 4 different datasets and 4 different embedded hardware platforms (x86…”
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EAST: Encoding-Aware Sparse Training for Deep Memory Compression of ConvNets by Grimaldi, Matteo, Peluso, Valentino, Calimera, Andrea

“…The implementation of Deep Convolutional Neural Networks (ConvNets) on tiny end-nodes with limited non-volatile memory space calls for smart compression…”
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Arbitrary-Precision Convolutional Neural Networks on Low-Power IoT Processors by Peluso, Valentino, Grimaldi, Matteo, Calimera, Andrea

“…The deployment of Convolutional Neural Networks (CNNs) on resource-constrained IoT devices calls for accurate model re-sizing and optimization. Among the…”
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Accelerating Deep Neural Networks via Semi-Structured Activation Sparsity by Grimaldi, Matteo, Ganji, Darshan C., Lazarevich, Ivan, Deeplite, Sudhakar Sah

“…The demand for efficient processing of deep neural networks (DNNs) on embedded devices is a significant challenge limiting their deployment. Exploiting…”
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