14 research outputs found
Class Brain Coprocessor based on Neuromorphic Circuit for Efficient Non-Formalization and Unstructured Information Processing
Class brain coprocessor is a type of coprocessor based on neuromorphic circuits that includes a memory module for storing training characteristics information, a processing module based on a hierarchical structure, and an encoder and decoder for input and output. This research proposes a memory module with a training characteristics storehouse and/or configurable training characteristics storehouse, and a processing module with a solidification functional network module and/or configurable functionality mixed-media network modules, which enhances the extended capability of the coprocessor. The proposed coprocessor employs distributed storage and concurrent collaborative processing, making it particularly suitable for handling non-formalization problems and unstructured information, as well as form problems and structured messages. The results show that this coprocessor significantly accelerates the speed of computers in processing class brain,informationificial intelligence, and reduces energy consumption while improving fault-tolerant ability, reducing programming complexity, and improving computing power
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
Near-sensor data analytics is a promising direction for IoT endpoints, as it
minimizes energy spent on communication and reduces network load - but it also
poses security concerns, as valuable data is stored or sent over the network at
various stages of the analytics pipeline. Using encryption to protect sensitive
data at the boundary of the on-chip analytics engine is a way to address data
security issues. To cope with the combined workload of analytics and encryption
in a tight power envelope, we propose Fulmine, a System-on-Chip based on a
tightly-coupled multi-core cluster augmented with specialized blocks for
compute-intensive data processing and encryption functions, supporting software
programmability for regular computing tasks. The Fulmine SoC, fabricated in
65nm technology, consumes less than 20mW on average at 0.8V achieving an
efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to
25MIPS/mW in software. As a strong argument for real-life flexible application
of our platform, we show experimental results for three secure analytics use
cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN
consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with
secured remote recognition in 5.74pJ/op; and seizure detection with encrypted
data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE
Transactions on Circuits and Systems - I: Regular Paper
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification
Classifiers that can be implemented on chip with minimal computational and
memory resources are essential for edge computing in emerging applications such
as medical and IoT devices. This paper introduces a machine learning model
based on oblique decision trees to enable resource-efficient classification on
a neural implant. By integrating model compression with probabilistic routing
and implementing cost-aware learning, our proposed model could significantly
reduce the memory and hardware cost compared to state-of-the-art models, while
maintaining the classification accuracy. We trained the resource-efficient
oblique tree with power-efficient regularization (ResOT-PE) on three neural
classification tasks to evaluate the performance, memory, and hardware
requirements. On seizure detection task, we were able to reduce the model size
by 3.4X and the feature extraction cost by 14.6X compared to the ensemble of
boosted trees, using the intracranial EEG from 10 epilepsy patients. In a
second experiment, we tested the ResOT-PE model on tremor detection for
Parkinson's disease, using the local field potentials from 12 patients
implanted with a deep-brain stimulation (DBS) device. We achieved a comparable
classification performance as the state-of-the-art boosted tree ensemble, while
reducing the model size and feature extraction cost by 10.6X and 6.8X,
respectively. We also tested on a 6-class finger movement detection task using
ECoG recordings from 9 subjects, reducing the model size by 17.6X and feature
computation cost by 5.1X. The proposed model can enable a low-power and
memory-efficient implementation of classifiers for real-time neurological
disease detection and motor decoding
Comparação de desempenho entre os modelos neurais ágeis ELM e WiSARD
Neural models are popular in machine learning. Agile neural models are a subset of this kind of models and are characterized by presenting a significantly faster training time, being applied mainly in online learning domains. Two examples of agile neural models are the Extreme Learning Machine (ELM), a single hidden layer feedforward neural network which synaptic weights do not need to be iteractively adjusted, and the Wilkes, Stonham and Aleksander Recognition Device (WiSARD), a weightless neural network model with multiple discriminators that use neurons based on RAM memory structures. In this work, a comparative study between ELM and WiSARD models is made, aiming to evaluate both models performance when applied to different datasets having different characteristics. The evaluation is made by comparing test accuracy, training and testing times metrics, as well as the amount of RAM memory consumed by the models.Modelos neurais são populares na área de aprendizado de máquina. Dentre os vários tipos de modelos desta classe, os modelos neurais ágeis se destacam por apresentarem tempo de treinamento consideravelmente inferior, sendo utilizados principalmente em domínios de aprendizado online. Dois exemplos deste tipo de modelo são a Extreme Learning Machine (ELM), que é uma rede neural com uma única camada oculta cujos pesos sinápticos não precisam ser ajustados, e a Wilkes, Stonham and Aleksander Recognition Device (WiSARD), um modelo de rede neural sem pesos com múltiplos discriminadores que utilizam neurônios implementados como estruturas de memória RAM. Neste trabalho, ´e realizado um estudo comparativo entre os modelos neurais ágeis ELM e WiSARD, visando avaliar o desempenho de ambos quando aplicados a diferentes conjuntos de dados com diferentes características. A avaliação é feita a partir da comparação das métricas de acurácia de teste, tempos de treinamento e de teste, além do uso de memória RAM dos dois modelos