Accelergy ADC Plug-In

The Accelergy ADC Plug-In provides estimates for the area and energy of Analog-Digital Converters (ADCs) for use in analog & mixed-signal accelerator design space exploration.

Models are based on statistical analysis of published ADC performance data in Boris Murmann's ADC Performance Survey [1]. The energy model is based on the observation that the maximum efficiency of an ADC is bounded by the sampling rate and the resolution [1], and the area model is based on regression analysis. Estimations are optimistic; they answer the question "what is the best possible ADC design for the given parameters?".

Quick Install

Clone the repository and install the plug-in using pip:

git clone https://github.com/Accelergy-Project/accelergy-adc-plug-in.git
cd accelergy-adc-plug-in
pip install .

Usage

Accelergy Interface

ADCs take the following parameters:

• adc_resolution: the number of bits in the ADC
• technology: the technology node in nm
• n_adcs: the number of ADCs working together, in the case of alternating ADCs
• throughput: the aggregate throughput of the ADCs, in samples per second

ADCs support the following actions:

• read or convert: Convert a single value from analog to digital. Note: if there are multiple ADCs, this is a single sample from a single ADC.

Exploring Tradeoffs

There are several tradeoffs available around ADC design:

• Lower-resolution ADCs are smaller and more energy-efficient.
• Using more ADCs in parallel allows for a lower frequency, but increases the area.
• Using fewer ADCs in parallel allows for a higher frequency. Up to a point, this will not increase the area or energy/area of the ADCs. However, at some this will result in an exponential increase in energy/area.
• Lower-resolution ADCs can run at higher frequencies before the exponential increase in energy/area occurs.

When the ADC plug-in runs, it will output a list of alternative design options. Each will report a number of ADCs and frequency needed to achieve the desired throughput, as well as the area and energy of the ADCs. You can then use this information to make tradeoffs between ADC resolution, frequency, and number of ADCs.

This is plug-in is the work of Tanner Andrulis & Ruicong Chen.

Updating the ADC Model

The generated ADC model is based on the data in Boris Murmann's survey [1], included in the submodule. This survey is updated periodically. The model can be update to reflect the most recent data by running the following:

pip3 install sklearn
pip3 install pandas
pip3 install numpy
git submodule update --init --recursive --remote
python3 update_model.py

This is only necessary if more recent data is published. If the data here is out of date, please open an issue or pull request.

References

[1] B. Murmann, "ADC Performance Survey 1997-2023," [Online]. Available: https://github.com/bmurmann/ADC-survey

License

This work is licensed under the MIT license. See license.txt for details.