Theses Doctoral

Investigation of Energy-Efficient Hybrid Analog/Digital Approximate Computation in Continuous Time

Guo, Ning

This work investigates energy-efficient approximate computation for solving differential equations. It extends the analog computing techniques to a new paradigm: continuous-time hybrid computation, where both analog and digital circuits operate in continuous time. In this approach, the time intervals in the digital signals contain important information. Unlike conventional synchronous digital circuits, continuous-time digital signals offer the benefits of adaptive power dissipation and no quantization noise.
Two prototype chips have been fabricated in 65 nm CMOS technology and tested successfully. The first chip is capable of solving nonlinear differential equations up to 4th order, and the second chip scales up to 16th order based on the first chip. Nonlinear functions are generated by a programmable, clockless, continuous-time 8-bit hybrid architecture (ADC+SRAM+DAC). Digitally-assisted calibration is used in all analog/mixed-signal blocks. Compared to the prior art, our chips makes possible arbitrary nonlinearities and achieves 16 times lower power dissipation, thanks to technology scaling and extensive use of class-AB analog blocks.
Typically, the unit achieves a computational accuracy of about 0.5% to 5% RMS, solution times from a fraction of 1 micro second to several hundred micro seconds, and total computational energy from a fraction of 1 nJ to hundreds of nJ, depending on equation details. Very significant advantages are observed in computational speed and energy (over two orders of magnitude and over one order of magnitude, respectively) compared to those obtained with a modern MSP430 microcontroller for the same RMS error.


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More About This Work

Academic Units
Electrical Engineering
Thesis Advisors
Tsividis, Yannis P.
Ph.D., Columbia University
Published Here
February 15, 2017