Hybrid Analog-Digital NLP Circuit for Real-Time Low-Power Optimization
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Solution Overview
Problem
Existing digital implementations of nonlinear programming (NLP) for power and energy applications are computationally complex, leading to real-time processing challenges and high power consumption, which limits their applicability and adoption.
Innovation Solution
A hybrid analog/digital computing circuit that combines a purely analog computing circuit with a low-power digital microcontroller, allowing for flexible programming of optimization techniques while minimizing overhead in cost and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital microcontrollers, FPGAs, GPUs, or CPUs are used to implement nonlinear programming control problems, then computational accuracy and programmability are improved, but power consumption increases and real-time processing capability deteriorates
Solution Approach 1:
The patent replaces digital computational systems (microcontrollers, FPGAs, GPUs, CPUs) with an analog circuit system that physically embodies the nonlinear programming problem. The analog circuit uses continuous voltage signals to represent variables and employs analog computing elements (operational amplifiers, resistors, capacitors, diodes) to perform optimization computations, thereby eliminating the high power consumption associated with digital processing while maintaining computational accuracy through physical realization of the optimization problem.
Solution Approach 2:
The patent transforms the computational problem from the digital domain to the analog domain by changing the fundamental parameter representation. Instead of using discrete digital numbers processed by high-power digital logic, the system uses continuous analog voltages and currents that naturally represent the optimization variables. This parameter transformation enables real-time continuous optimization with minimal power consumption, as the analog circuit continuously adjusts voltages to satisfy the Karush-Kuhn-Tucker conditions without requiring iterative digital computation.
2Adaptability or versatility
If digital microcontrollers, FPGAs, GPUs, or CPUs are used to implement nonlinear programming control problems, then programmability and flexibility are improved, but real-time processing speed deteriorates
Solution Approach 1:
The patent replaces sequential digital computation with parallel analog computation. The analog circuit performs all optimization computations simultaneously through physical laws governing circuit behavior, achieving real-time processing speeds that are orders of magnitude faster than digital systems. The continuous nature of analog signals allows the circuit to naturally converge to the optimal solution without discrete time steps, enabling true real-time control.
3Use of energy by moving object
If purely analog computing circuits are used to enable lower power and faster computing, then power consumption decreases and processing speed improves, but programmability and flexibility deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where a digital microcontroller periodically measures voltage signals from the analog circuit and updates the cost function parameters based on the current state. This hybrid feedback loop maintains the low power consumption and fast processing of the analog circuit while providing programmability and adaptability through digital control. The microcontroller can reprogram the cost function by adjusting reference voltages or component values, enabling flexibility without sacrificing the energy efficiency of analog computation.
Data Source
AI summary
A hybrid analog-digital electronic circuit for solving non-linear programming problems includes an analog circuit and a digital microcontroller interconnected to each other by an analog-to-digital converter (ADC) and a digital-to-analog converter (DAC). The analog circuit physically realizes a nonlinear programming problem (NLP) where voltages in the analog circuit represent variables in the NLP, and the interconnection of the analog circuit components enforce Karush-Kuhn-Tucker (KKT) conditions on the variables, such that the voltages in the analog circuit that represent the variables of the NLP naturally converge to an optimal and feasible solution of the NLP. The digital microcontroller sets the voltages in the analog circuit at particular nodes in the analog circuit through the DAC, where the voltages set at the particular nodes determine a precise cost function to be minimized by the analog circuit, where the voltages set at the particular nodes are computed by the digital microcontroller based on measurements obtained from the analog circuit through the ADC.