An overview of machine learning within embedded and mobile devices–optimizations and applications
Embedded systems technology is undergoing a phase of transformation owing to the novel
advancements in computer architecture and the breakthroughs in machine learning …
advancements in computer architecture and the breakthroughs in machine learning …
A survey of methods for low-power deep learning and computer vision
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the
most accurate DNNs require millions of parameters and operations, making them energy …
most accurate DNNs require millions of parameters and operations, making them energy …
A 0.086-mm 12.7-pJ/SOP 64k-Synapse 256-Neuron Online-Learning Digital Spiking Neuromorphic Processor in 28-nm CMOS
Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
(SNNs) uncovers new opportunities for low-power processing of sensory data in …
A survey on methods and theories of quantized neural networks
Y Guo - arXiv preprint arXiv:1808.04752, 2018 - arxiv.org
Deep neural networks are the state-of-the-art methods for many real-world tasks, such as
computer vision, natural language processing and speech recognition. For all its popularity …
computer vision, natural language processing and speech recognition. For all its popularity …
hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices
F Fahim, B Hawks, C Herwig, J Hirschauer… - arXiv preprint arXiv …, 2021 - arxiv.org
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient
devices and systems are extremely valuable across a broad range of application domains …
devices and systems are extremely valuable across a broad range of application domains …
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
Although the quest for more accurate solutions is pushing deep learning research towards
larger and more complex algorithms, edge devices demand efficient inference and therefore …
larger and more complex algorithms, edge devices demand efficient inference and therefore …
Hand-gesture recognition based on EMG and event-based camera sensor fusion: A benchmark in neuromorphic computing
Hand gestures are a form of non-verbal communication used by individuals in conjunction
with speech to communicate. Nowadays, with the increasing use of technology, hand …
with speech to communicate. Nowadays, with the increasing use of technology, hand …
PULP-NN: Accelerating quantized neural networks on parallel ultra-low-power RISC-V processors
We present PULP-NN, an optimized computing library for a parallel ultra-low-power tightly
coupled cluster of RISC-V processors. The key innovation in PULP-NN is a set of kernels for …
coupled cluster of RISC-V processors. The key innovation in PULP-NN is a set of kernels for …
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
E Govorkova, E Puljak, T Aarrestad, T James… - Nature Machine …, 2022 - nature.com
To study the physics of fundamental particles and their interactions, the Large Hadron
Collider was constructed at CERN, where protons collide to create new particles measured …
Collider was constructed at CERN, where protons collide to create new particles measured …
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor With Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight quantization as a
means to increase the resourceand power-efficiency of hardware devices. As full on-chip …
means to increase the resourceand power-efficiency of hardware devices. As full on-chip …