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07/31/2023
PATRONoC: Parallel AXI Transport Reducing Overhead for Networks-on-Chip targeting Multi-Accelerator DNN Platforms at the Edge
Emerging deep neural network (DNN) applications require high-performance...
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06/08/2023
Precision-aware Latency and Energy Balancing on Multi-Accelerator Platforms for DNN Inference
The need to execute Deep Neural Networks (DNNs) at low latency and low p...
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05/25/2023
Benchmarking and modeling of analog and digital SRAM in-memory computing architectures
In-memory-computing is emerging as an efficient hardware paradigm for de...
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04/20/2023
SALSA: Simulated Annealing based Loop-Ordering Scheduler for DNN Accelerators
To meet the growing need for computational power for DNNs, multiple spec...
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01/09/2023
TinyVers: A Tiny Versatile System-on-chip with State-Retentive eMRAM for ML Inference at the Extreme Edge
Extreme edge devices or Internet-of-thing nodes require both ultra-low p...
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12/20/2022
Towards Heterogeneous Multi-core Accelerators Exploiting Fine-grained Scheduling of Layer-Fused Deep Neural Networks
To keep up with the ever-growing performance demand of neural networks, ...
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12/10/2022
DeFiNES: Enabling Fast Exploration of the Depth-first Scheduling Space for DNN Accelerators through Analytical Modeling
DNN workloads can be scheduled onto DNN accelerators in many different w...
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08/26/2022
Hardware-aware mobile building block evaluation for computer vision
In this work we propose a methodology to accurately evaluate and compare...
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08/10/2021
Survey and Benchmarking of Precision-Scalable MAC Arrays for Embedded DNN Processing
Reduced-precision and variable-precision multiply-accumulate (MAC) opera...
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02/27/2021
Acceleration of probabilistic reasoning through custom processor architecture
Probabilistic reasoning is an essential tool for robust decision-making ...
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02/27/2021
ProbLP: A framework for low-precision probabilistic inference
Bayesian reasoning is a powerful mechanism for probabilistic inference i...
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09/21/2020
Feed-Forward On-Edge Fine-tuning Using Static Synthetic Gradient Modules
Training deep learning models on embedded devices is typically avoided s...
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07/22/2020
ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Framework
Building efficient embedded deep learning systems requires a tight co-de...
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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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12/17/2018
A multi-layered energy consumption model for smart wireless acoustic sensor networks
Smart sensing is expected to become a pervasive technology in smart citi...
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04/16/2018
BinarEye: An Always-On Energy-Accuracy-Scalable Binary CNN Processor With All Memory On Chip in 28nm CMOS
This paper introduces BinarEye: a digital processor for always-on Binary...
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03/13/2018
Resource aware design of a deep convolutional-recurrent neural network for speech recognition through audio-visual sensor fusion
Today's Automatic Speech Recognition systems only rely on acoustic signa...
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11/01/2017
Minimum Energy Quantized Neural Networks
This work targets the automated minimum-energy optimization of Quantized...
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06/16/2016
A 0.3-2.6 TOPS/W Precision-Scalable Processor for Real-Time Large-Scale ConvNets
A low-power precision-scalable processor for ConvNets or convolutional n...
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03/22/2016