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Starting in the early 2000s, sophisticated technologies have been developed for the rational construction of synthetic genetic networks that implement specified logical functionalities. Despite impressive progress, however, the scaling necessary in order to achieve greater computational power has been hampered by many constraints, including repressor toxicity and the lack of large sets of mutually orthogonal repressors. As a consequence, a typical circuit contains no more than roughly seven repressor-based gates per cell. A possible way around this scalability problem is to distribute the computation among multiple cell types, each of which implements a small subcircuit, which communicate among themselves using diffusible small molecules (DSMs). Examples of DSMs are those employed by quorum sensing systems in bacteria. This paper focuses on systematic ways to implement this distributed approach, in the context of the evaluation of arbitrary Boolean functions. The unique characteristics of genetic circuits and the properties of DSMs require the development of new Boolean synthesis methods, distinct from those classically used in electronic circuit design. In this work, we propose a fast algorithm to synthesize distributed realizations for any Boolean function, under constraints on the number of gates per cell and the number of orthogonal DSMs. The method is based on an exact synthesis algorithm to find the minimal circuit per cell, which in turn allows us to build an extensive database of Boolean functions up to a given number of inputs. For concreteness, we will specifically focus on circuits of up to 4 inputs, which might represent, for example, two chemical inducers and two light inputs at different frequencies. Our method shows that, with a constraint of no more than seven gates per cell, the use of a single DSM increases the total number of realizable circuits by at least 7.58-fold compared to centralized computation. Moreover, when allowing two DSM’s, one can realize 99.995% of all possible 4-input Boolean functions, still with at most 7 gates per cell. The methodology introduced here can be readily adapted to complement recent genetic circuit design automation software. A toolbox that uses the proposed algorithm was created and made available at https://github. com/sontaglab/DBC/.
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Exploring target function approximation for stochastic circuit minimization
Stochastic computing (SC) is an emerging paradigm for designing circuits to perform complicated computation with simple circuitry. Although SC circuits have small area and critical-path delay, due to the need of many clock cycles to perform computation, they have a large overall latency and energy consumption. One solution to this problem is to further minimize the circuits. In this work, we explore target function approximation to derive an SC circuit with significantly reduced area and delay. We propose two static methods that first construct a set of functions close to the given target function and then select the best synthesized SC circuit realizing one of these functions. We also propose an efficient dynamic method that simultaneously searches for the best approximated target function and the corresponding minimized SC circuit. The experimental results show that on average, our dynamic method dramatically reduces the area, critical-path delay, and area-delay product of the SC circuits by 80%, 59%, and 91%, respectively, over the state-of-the-art Maclaurin polynomial-based method for a given error bound of 2%. The code of our methods is made open-source.
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- Award ID(s):
- 2006704
- PAR ID:
- 10220236
- Date Published:
- Journal Name:
- ICCAD '20: Proceedings of the 39th International Conference on Computer-Aided Design
- Page Range / eLocation ID:
- 1 to 9
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3400302.3415701
