A Model-to-Circuit Compiler for Evaluation of DNN Accelerators based on Systolic Arrays and Multibit Emerging Memories

Knödtel J, Fritscher M, Reiser D, Fey D, Breiling M, Reichenbach M (2020)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: IEEE

Pages Range: 1--6

Conference Proceedings Title: 9th International Conference on Modern Circuits and Systems Technologies, {MOCAST} 2020, Bremen, Germany, September 7-9, 2020

Event location: Bremen DE

ISBN: 9781728166872

DOI: 10.1109/MOCAST49295.2020.9200241

Abstract

With the emergence of DNN accelerators the main focus of such systems usually lies on utilizing local memories and reducing the size of the processed data, since delay and energy consumption are dominated by data transfer. Utilizig emerging memory technologies, such as ReRAMs, these goals might be attained much easier, due to advantageous non-functional and functional properties.One of the key drawbacks of such systems are reliability and variability of devices of such technologies. To certain degree, DNNs are resilient to soft and hard errors in their memory cells, so these issues might be surmountable depending on the device properties, but eludes trivial analyses known from the digital domain. Here the dynamic behavior of the devices comes into play and must be simulated in order to get a decent degree of confidence on the reliability of the hardware and therefore also yield.In order to tackle this issue we present an accelerator architecture and matching analysis pipeline that allows the user to specify and train a net topology and then test the design against some input activations with different randomized device properties. Using this approach we can estimate the inference results and other system and algorithm level properties in presence of different device level properties which might for example be extracted from real world measurements. Such a system can help the user in the design of their net or give them hints on the required device properties for a given net or aid them in evaluating existing designs.

Authors with CRIS profile

Johannes Knödtel Lehrstuhl für Informatik 3 (Rechnerarchitektur) Markus Fritscher Lehrstuhl für Informatik 4 (Verteilte Systeme und Betriebssysteme) Daniel Reiser Lehrstuhl für Informatik 3 (Rechnerarchitektur) Dietmar Fey Lehrstuhl für Informatik 3 (Rechnerarchitektur) Marc Reichenbach Lehrstuhl für Informatik 3 (Rechnerarchitektur)

Involved external institutions

Fraunhofer-Institut für Integrierte Schaltungen (IIS) DE Germany (DE)

How to cite

APA:

Knödtel, J., Fritscher, M., Reiser, D., Fey, D., Breiling, M., & Reichenbach, M. (2020). A Model-to-Circuit Compiler for Evaluation of DNN Accelerators based on Systolic Arrays and Multibit Emerging Memories. In 9th International Conference on Modern Circuits and Systems Technologies, {MOCAST} 2020, Bremen, Germany, September 7-9, 2020 (pp. 1--6). Bremen, DE: IEEE.

MLA:

Knödtel, Johannes, et al. "A Model-to-Circuit Compiler for Evaluation of DNN Accelerators based on Systolic Arrays and Multibit Emerging Memories." Proceedings of the 9th International Conference on Modern Circuits and Systems Technologies, MOCAST 2020, Bremen IEEE, 2020. 1--6.

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