Arm Limited

Nvidia Corporation

Zhiyao Xie

Shidhartha Das

Xiaoqing Xu

Haoxing Ren

Brucek Kurdo Khailany

Matthew Paul Elwood

Matthew James Walker

Kumara Guru Palaniswamy

Sheng Ye

A power meter for measuring power usage in a circuit includes preprocessor and a weighting network. The pre-processor is configured to receive toggle data for a number of power proxy signals in the circuit for a plurality of clock cycles of the circuit in a first time window. The power proxy signals and weighting values are determined automatically from simulated or emulated toggle data. For each power proxy signal, the pre-processor averages the toggle data over one or more clock cycles in one or more second time windows, within the first time window, to provide averaged toggle data, and outputs the averaged toggle data for each second time window. The weighting network is configured to combine the averaged toggle data from the power proxy signals, based on a set of weight values, to provide a measure of the power usage.

Method and apparatus for power measurement in electronic circuit design and analysis

An integrated circuit includes a first circuit and a power meter coupled to the first circuit at selected proxy locations. The power meter includes circuitry for generating toggle data, such as signal transitions or signal levels, from signals at the proxy locations and combiner circuitry for combining the toggle data in a first time window with a set of weight value to produce a measure of power usage in the first circuit. The proxy locations and weight values are selected automatically based on simulated or emulated signals from a larger set of locations in the first circuit and associated power usage in the first circuit.

Method and apparatus for on-chip power metering using automated selection of signal power proxies

IR drop predictions are obtained using a maximum convolutional neural network. A circuit structure is partitioned into a grid. For cells of the circuit structure in sub-intervals of a clock period, power consumption of the cell is amortized into a set of grid tiles that include portions of the cell, thus forming a set of power maps. The power maps are applied to a neural network to generate IR drop predictions for the circuit structure.

IR drop prediction with maximum convolutional neural network

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Durham, NC, United States of America

Zhiyao Xie