3,402 research outputs found
Racing to Learn: Statistical Inference and Learning in a Single Spiking Neuron with Adaptive Kernels
This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a
simple spiking neuron model that performs statistical inference and
unsupervised learning of spatiotemporal spike patterns. SKAN is the first
proposed neuron model to investigate the effects of dynamic synapto-dendritic
kernels and demonstrate their computational power even at the single neuron
scale. The rule-set defining the neuron is simple there are no complex
mathematical operations such as normalization, exponentiation or even
multiplication. The functionalities of SKAN emerge from the real-time
interaction of simple additive and binary processes. Like a biological neuron,
SKAN is robust to signal and parameter noise, and can utilize both in its
operations. At the network scale neurons are locked in a race with each other
with the fastest neuron to spike effectively hiding its learnt pattern from its
neighbors. The robustness to noise, high speed and simple building blocks not
only make SKAN an interesting neuron model in computational neuroscience, but
also make it ideal for implementation in digital and analog neuromorphic
systems which is demonstrated through an implementation in a Field Programmable
Gate Array (FPGA).Comment: In submission to Frontiers in Neuroscienc
YodaNN: An Architecture for Ultra-Low Power Binary-Weight CNN Acceleration
Convolutional neural networks (CNNs) have revolutionized the world of
computer vision over the last few years, pushing image classification beyond
human accuracy. The computational effort of today's CNNs requires power-hungry
parallel processors or GP-GPUs. Recent developments in CNN accelerators for
system-on-chip integration have reduced energy consumption significantly.
Unfortunately, even these highly optimized devices are above the power envelope
imposed by mobile and deeply embedded applications and face hard limitations
caused by CNN weight I/O and storage. This prevents the adoption of CNNs in
future ultra-low power Internet of Things end-nodes for near-sensor analytics.
Recent algorithmic and theoretical advancements enable competitive
classification accuracy even when limiting CNNs to binary (+1/-1) weights
during training. These new findings bring major optimization opportunities in
the arithmetic core by removing the need for expensive multiplications, as well
as reducing I/O bandwidth and storage. In this work, we present an accelerator
optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core
area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm and with a power
dissipation of 895 {\mu}W in UMC 65 nm technology at 0.6 V. Our accelerator
significantly outperforms the state-of-the-art in terms of energy and area
efficiency achieving 61.2 TOp/s/[email protected] V and 1135 GOp/s/[email protected] V, respectively
Efficient channel equalization algorithms for multicarrier communication systems
Blind adaptive algorithm that updates time-domain equalizer (TEQ) coefficients by Adjacent Lag Auto-correlation Minimization (ALAM) is proposed to shorten the channel for multicarrier modulation (MCM) systems. ALAM is an addition to the family of several existing correlation based algorithms that can achieve similar or better performance to existing algorithms with lower complexity. This is achieved by designing a cost function without the sum-square and utilizing symmetrical-TEQ property to reduce the complexity of adaptation of TEQ to half of the existing one. Furthermore, to avoid the limitations of lower unstable bit rate and high complexity, an adaptive TEQ using equal-taps constraints (ETC) is introduced to maximize the bit rate with the lowest complexity. An IP core is developed for the low-complexity ALAM (LALAM) algorithm to be implemented on an FPGA. This implementation is extended to include the implementation of the moving average (MA) estimate for the ALAM algorithm referred as ALAM-MA. Unit-tap constraint (UTC) is used instead of unit-norm constraint (UNC) while updating the adaptive algorithm to avoid all zero solution for the TEQ taps. The IP core is implemented on Xilinx Vertix II Pro XC2VP7-FF672-5 for ADSL receivers and the gate level simulation guaranteed successful operation at a maximum frequency of 27 MHz and 38 MHz for ALAM-MA and LALAM algorithm, respectively. FEQ equalizer is used, after channel shortening using TEQ, to recover distorted QAM signals due to channel effects. A new analytical learning based framework is proposed to jointly solve equalization and symbol detection problems in orthogonal frequency division multiplexing (OFDM) systems with QAM signals. The framework utilizes extreme learning machine (ELM) to achieve fast training, high performance, and low error rates. The proposed framework performs in real-domain by transforming a complex signal into a single 2–tuple real-valued vector. Such transformation offers equalization in real domain with minimum computational load and high accuracy. Simulation results show that the proposed framework outperforms other learning based equalizers in terms of symbol error rates and training speeds
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
Addressing Imperfect Symmetry: a Novel Symmetry-Learning Actor-Critic Extension
Symmetry, a fundamental concept to understand our environment, often
oversimplifies reality from a mathematical perspective. Humans are a prime
example, deviating from perfect symmetry in terms of appearance and cognitive
biases (e.g. having a dominant hand). Nevertheless, our brain can easily
overcome these imperfections and efficiently adapt to symmetrical tasks. The
driving motivation behind this work lies in capturing this ability through
reinforcement learning. To this end, we introduce Adaptive Symmetry Learning
(ASL) \unicode{x2013} a model-minimization actor-critic extension that
addresses incomplete or inexact symmetry descriptions by adapting itself during
the learning process. ASL consists of a symmetry fitting component and a
modular loss function that enforces a common symmetric relation across all
states while adapting to the learned policy. The performance of ASL is compared
to existing symmetry-enhanced methods in a case study involving a four-legged
ant model for multidirectional locomotion tasks. The results demonstrate that
ASL is capable of recovering from large perturbations and generalizing
knowledge to hidden symmetric states. It achieves comparable or better
performance than alternative methods in most scenarios, making it a valuable
approach for leveraging model symmetry while compensating for inherent
perturbations
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