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Found 25 papers, 3 papers with code
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler
Hierarchical temporal memory (HTM) is an emerging machine learning algorithm, with the potential to provide a means to perform predictions on spatiotemporal data.
Unsupervised Learning in Neuromemristive Systems
Neuromemristive systems (NMSs) currently represent the most promising platform to achieve energy efficient neuro-inspired computation.
Convolutional Drift Networks for Video Classification
Analyzing spatio-temporal data like video is a challenging task that requires processing visual and temporal information effectively.
On the Statistical Challenges of Echo State Networks and Some Potential Remedies
We create, develop and implement a family of predictably optimal robust and stable ensemble of Echo State Networks via regularizing the training and perturbing the input.
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit
Performing the inference step of deep learning in resource constrained environments, such as embedded devices, is challenging.
Mod-DeepESN: Modular Deep Echo State Network
Neuro-inspired recurrent neural network algorithms, such as echo state networks, are computationally lightweight and thereby map well onto untethered devices.
Neuromorphic Architecture for the Hierarchical Temporal Memory
The spatial pooler architecture is synthesized on Xilinx ZYNQ-7, with 91. 16% classification accuracy for MNIST and 90\% accuracy for EUNF, with noise.
PositNN: Tapered Precision Deep Learning Inference for the Edge
Conventional reduced-precision numerical formats, such as fixed-point and floating point, cannot accurately represent deep neural network parameters with a nonlinear distribution and small dynamic range.
Deep Positron: A Deep Neural Network Using the Posit Number System
We propose a precision-adaptable FPGA soft core for exact multiply-and-accumulate for uniform comparison across three numerical formats, fixed, floating-point and posit.
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis
A memristor that is suitable for emulating the HTM synapses is identified and a new Z-window function is proposed.
Performance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks
Our results indicate that posits are a natural fit for DNN inference, outperforming at $\leq$8-bit precision, and can be realized with competitive resource requirements relative to those of floating point.
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting
Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry.
Deep Learning Training on the Edge with Low-Precision Posits
Recently, the posit numerical format has shown promise for DNN data representation and compute with ultra-low precision ([5.. 8]-bit).
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge
Additionally, the framework is amenable for different quantization approaches and supports mixed-precision floating point and fixed-point numerical formats.
Metaplasticity in Multistate Memristor Synaptic Networks
In the $128\times128$ network, it is observed that the number of input patterns the multistate synapse can classify is $\simeq$ 2. 1x that of a simple binary synapse model, at a mean accuracy of $\geq$ 75% .
SIRNet: Understanding Social Distancing Measures with Hybrid Neural Network Model for COVID-19 Infectious Spread
The SARS-CoV-2 infectious outbreak has rapidly spread across the globe and precipitated varying policies to effectuate physical distancing to ameliorate its impact.
Populations and Evolution
End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction
We also illustrate that the system offers 3. 46X reduction in latency and 77. 02X reduction in power consumption when compared to a custom CMOS digital design implemented at the same technology node.
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT
In this research, we propose a new low-precision framework, TENT, to leverage the benefits of a tapered fixed-point numerical format in TinyML models.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Design Principles for Lifelong Learning AI Accelerators
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI).
Continual Learning: Applications and the Road Forward
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past.
Continual Learning and Catastrophic Forgetting
This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data.
Probabilistic Metaplasticity for Continual Learning with Memristors
The proposed mechanism eliminates high-precision modification to weight magnitude and consequently, high-precision memory for gradient accumulation.
Watch Your Step: Optimal Retrieval for Continual Learning at Scale
One of the most widely used approaches in continual learning is referred to as replay.
