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Low-power brain-inspired hardware systems have gained significant traction in recent years. They offer high energy efficiency and massive parallelism due to the distributed and asynchronous nature of neural computation through low-energy spikes. One such platform is the IBM TrueNorth Neurosynaptic System. Recently TrueNorth compatible representation learning algorithms have emerged, achieving close...
Spike-Timing Dependent Plasticity (STDP), the canonical learning rule for spiking neural networks (SNN), is gaining tremendous interest because of its simplicity, efficiency and biological plausibility. However, to date, multilayer feed-forward networks of spiking neurons are either only partially trained using STDP or pre-trained using traditional deep neural networks which are converted to deep...
With the proliferation of application specific accelerators, the use of heterogeneous clusters is rapidly increasing. Consisting of processors with different architectures, a heterogeneous cluster aims at providing different performance and cost tradeoffs for different types of workloads. In order to achieve peak performance, software running on heterogeneous cluster needs to be designed carefully...
Spiking neural networks are rapidly gaining popularity for their ability to perform efficient computation akin to the way a brain processes information. It has the potential to achieve low cost and high energy efficiency due to the distributed nature of neural computation and the use of low energy spikes for information exchange. A stochastic spiking neural network naturally can be used to realize...
The ability of neural networks to perform pattern recognition, classification and associative memory, is essential to applications such as image and speech recognition, natural language understanding, decision making etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed train of spikes, which allows learning through the spike-timing dependent plasticity (STDP) property...
The emerging field of neuromorphic computing is offering a possible pathway for approaching the brain's computing performance and energy efficiency for cognitive applications such as pattern recognition, speech understanding, natural language processing etc. In spiking neural networks (SNNs), information is encoded as sparsely distributed spike trains, enabling learning through the spike-timing dependent...
Although existing optical character recognition (OCR) tools can achieve excellent performance in text image detection and pattern recognition, they usually require a clean input image. Most of them do not perform well when the image is partially occluded or smudged. Humans are able to tolerate much worse image quality during reading because the perception errors can be corrected by the knowledge in...
Although existing optical character recognition (OCR) tools can achieve excellent performance in text image detection and pattern recognition, they usually require a clean input image. Most of them do not perform well when the image is partially occluded or smudged. Humans are able to tolerate much worse image quality during reading because the perception errors can be corrected by the knowledge in...
Neuromorphic computing systems refer to the computing architecture inspired by the working mechanism of human brains. The rapidly reducing cost and increasing performance of state-of-the-art computing hardware allows large-scale implementation of machine intelligence models with neuromorphic architectures and opens the opportunity for new applications. One such computing hardware is Intel Xeon Phi...
In this paper, we describe new methods for the simulation, measurement and representation of sEMG signals. With regard to simulation, we choose a 2-D state space model and suggest a 3-D model which can account for in- homogeneities, nonlinearities and memory in the medium and which can be hardware accelerated through FPGA. With regard to measurement we use surface electrodes with a new amplifier circuit...
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