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.

ResourceEfficient Logarithmic Number Scale Arithmetic for SPN Inference on FPGAs FPGAs have been successfully used for the implementation of dedicated accelerators for a wide range of machine learning problems. The inference in socalled SumProduct Networks can also be accelerated efficiently using a pipelined FPGA architecture. However, as SumProduct Networks compute exact probability values, the required arithmetic precision poses different challenges than those encountered with Neural Networks. In previous work, this precision was maintained by using doubleprecision floatingpoint number formats, which are expensive to implement in FPGAs. In this work, we propose the use of a logarithmic number system format tailored specifically towards the inference in SumProduct Networks. The evaluation of our optimized arithmetic hardware operators shows that the use of logarithmic number formats allows to save up to 50% hardware resources compared to doubleprecision floating point, while maintaining sufficient precision for SPN inference at almost identical performance.