System and methods for mixed -signal computing

Abstract

Systems and methods of implementing a mixed-signal integrated circuit includes sourcing, by a reference signal source, a plurality of analog reference signals along a shared signal communication path to a plurality of local accumulators; producing an electrical charge, at each of the plurality of local accumulators, based on each of the plurality of analog reference signals; adding or subtracting, by each of the plurality of local accumulators, the electrical charge to an energy storage device of each of the plurality of local accumulators over a predetermined period; summing along the shared communication path the electrical charge from the energy storage device of each of the plurality of local accumulators at an end of the predetermined period; and generating an output based on a sum of the electrical charge from each of the plurality of local accumulators.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional Application No. 62 / 559,354, filed 15 Sep. 2017, which is incorporated in its entirety by this reference.TECHNICAL FIELD[0002]The inventions herein relate generally to the integrated circuitry architecture field, and more specifically to new and useful mixed-signal integrated circuits and methods of computing mixed-signals in the integrated circuitry architecture field.BACKGROUND[0003]Today, the various implementations of artificial intelligence are driving innovation in many fields of technology. Artificial intelligence (AI) systems and artificial intelligence models (including algorithms) are defined by many system architectures and models that enable machine learning (deep learning), reasoning, inferential capacities, and large data processing capabilities of a machine (e.g., a computer and / or a computing server). These AI systems and models are often trained intensively to perform...

AI Technical Summary

Benefits of technology

[0022]In one embodiment, the reference signal source comprises a segmented global signal reference source comprising two or more signal reference sources that are combined and generate high resolution analog reference signals, wherein the plurality of analog reference signals comprise a plurality

Problems solved by technology

Still, while neural network models or algorithms may not require a same amount of compute resources, as required in a training phase, deploying a neural network model or algorithm in the field continues to require significant circuitry area, energy, and compute power to classify data and infer or predict a result.

However, latency problems are manifest when these remote artificial intelligence processing systems are used in computing inferences and the like for remote, edge computing devices or in field devices.

That is, when these traditional remote systems seek to implement a neural network model for generating inferences to be used in remote field devices, there are unavoidable delays in receiving input data fr

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