Although Artificial Neural Networks (ANNs) are inspired by biological neural systems, most of ANNs today are implemented with digital circuitry and use binary values in computation. In recent years, analog-based neuromorphic system has gained lots of attention as it provides a natural interface for brain-machine interaction.

Multiplyaccumulate neural network

Request PDF | New Flexible Multiple-Precision Multiply-Accumulate Unit for Deep Neural Network Training and Inference | In this paper, a new flexible multiple-precision.

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If the slope is a lower value, the neural network is confident in its prediction, and less movement of the weights is needed. If the slope is of a higher value, then the neural network's predictions are closer to .50, or 50% (The highest slope value possible for the sigmoid function is at x=0 and y=.5. y is the prediction.). This means the.

Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for image, voice, and video processing. CNNs require large amounts of processing capacity and memory bandwidth. Hardware accelerators have been proposed for. We also present the heterogeneous multiply-accumulate (MAC) unit based design approach where some of the MAC units are designed larger with shorter critical path delays for robustness to aggressive voltage scaling while other MAC units are designed relatively smaller. Implementations of artificial neural networks that borrow analogue techniques could potentially offer low-power alternatives to fully digital approaches(1-3). One notable example is in-memory computing based on crossbar arrays of non-volatile memories(4-7) that execute, in an analogue manner, multiply-accumulate operations prevalent in artificial neural networks. The inherent heavy computation of deep neural networks prevents their widespread applications. A widely used method for accelerating model inference is quantization, by replacing the input operands of a network using fixed-point values. Approximate Multiply-Accumulate Array for Convolutional Neural Networks on FPGA. / Wang, Ziwei; Trefzer, Martin A; Bale, Simon J et al. 2019 14th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC). 2019. p. 35-42. Approximate Multiply-Accumulate Array for Convolutional Neural Networks on FPGA. / Wang, Ziwei; Trefzer, Martin A; Bale, Simon J et al. 2019 14th International Symposium on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC). 2019. p. 35-42.

Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial Intelligence (AI) applications. Their ability to go beyond human precision has made these networks a milestone in the history of AI. However, while on the one hand they present cutting edge performance, on the other hand they require enormous computing power. For this.

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