A Programmable Calculation Unit Employing Memcapacitorbased Neuromorphic Circuit An efficient calculation unit is developed on the basis of programmable neuromorphic circuit for implementing arbitrary mathematical functions. To retrieve any specific function approximately, the regression algorithm through neural network (NN) is realized by a compact analog circuitry. The memcapacitor technology is associated to NeuronMOS structure, which couples multiple memcapacitors on the floating gate of a MOS transistor to achieve multiplyaccumulation (MAC) operation. In this manner, each synapse of NN is emulated by only one memcapacitor device and the weight is postfabrication programmable due to the memcapacitive characteristics of memcapacitor. Two types of neurons including sigmoid and linear are implemented by differential pairs with NeuronMOS transistors. For proofofconcept, the memcapacitorbased approximate calculation unit (MCACU) for arbitrary twooperand computations is achieved with 461 devices. From the real circuit simulation results, all the example functions are retrieved with the maximum inaccuracy of 7.8%. The energy and devicecount of MCACU are only 36.7% and 15.7% of that of stateoftheart works in approximate computing, respectively.

Exploring Numerical Calculations with CalcNet Neural networks are not great generalizers outside their training range i.e. they are good at capturing bias but might miss the overall concept. Important issues with neural networks is that when testing data goes outside training range they fail to predict accurate results. Hence, they loose the ability to generalize a concept. For systematic numeric exploration neural accumulators (NAC) and neural arithmetic logic unit(NALU) are proposed which performs excellent for simple arithmetic operations. But, major limitation with these units is that they canu0027t handle complex mathematical operations u0026 equations. For example, NALU can predict accurate results for multiplication operation but not for factorial function which is essentially composition of multiplication operations only. It is unable to comprehend pattern behind an expression when composition of operations are involved. Hence, we propose a new neural network structure effectively which takes in complex compositional mathematical operations and produces best possible results with small NALU based neural networks as its pluggable modules which evaluates these expression at unitary level in a bottomup manner. We call this effective neural network as CalcNet, as it helps in predicting accurate calculations for complex numerical expressions even for values that are out of training range. As part of our study we applied this network on numerically approximating complex equations, evaluating biquadratic equations and tested reusability of these modules. We arrived at far better generalizations for complex arithmetic extrapolation tasks as compare to both only NALU layer based neural networks and simple feed forward neural networks. Also, we achieved even better results for our golden ratio based modified NAC and NALU structures for both interpolating and extrapolating tasks in all evaluation experiments. Finally, from reusability standpoint this model demonstrate strong invariance for making predictions on different tasks.