S. J. Dat Tran and C. Teuscher, “Deep Memcapacitive Network,” 2020 IEEE 15th International Conference on Nano/Micro Engineered and Molecular System (NEMS), San Diego, CA, USA, 2020, pp. 200-205, doi: https://doi.org/10.1109/NEMS50311.2020.9265561
Category Archives: Publication
Alife 2020 paper
Our Alife 2020 paper was accepted for publication.
H. Nguyen, P. Banda, D. Stefanovic, and C. Teuscher, Reservoir Computing with Random Chemical Systems, Proceedings of The 2020 Conference on Artificial Life, MIT Press, pp. 491-499, 2020. https://doi.org/10.1162/isal_a_00324
Abstract: Top-down engineering of biomolecular circuits to perform specific computational tasks is notoriously hard and time-consuming. Current circuits have limited complexity and are brittle and application-specific. Here we propose an alternative: we design and test a bottom-up constructed Reservoir Computer (RC) that uses random chemical circuits inspired by DNA strand displacement reactions. This RC has the potential to be implemented easily and trained for various tasks. We describe and simulate it by means of a Chemical Reaction Network (CRN) and evaluate its performance on three computational tasks: the Hamming distance and a short- as well as a long-term memory. Compared with the deoxyribozyme oscillator RC model simulated by Yahiro et al., our random chemical RC performs 75.5% better for the short-term and 67.2% better for the long-term memory task. Our model requires an 88.5% larger variety of chemical species, but it relies on random chemical circuits, which can be more easily realized and scaled up. Thus, our novel random chemical RC has the potential to simplify the way we build adaptive biomolecular circuits.
tlab alumnus publishes in Nature
tlab alumnus Dr. Jens Bürger publishes in Nature Humanities and Social Sciences Communications. Way to go!
Bürger, J., Laguna-Tapia, A. Individual homogenization in large-scale systems: on the politics of computer and social architectures. Palgrave Commun 6, 47 (2020). https://doi.org/10.1057/s41599-020-0425-4
Abstract: One determining characteristic of contemporary sociopolitical systems is their power over increasingly large and diverse populations. This raises questions about power relations between heterogeneous individuals and increasingly dominant and homogenizing system objectives. This article crosses epistemic boundaries by integrating computer engineering and a historicalphilosophical approach making the general organization of individuals within large-scale systems and corresponding individual homogenization intelligible. From a versatile archeological-genealogical perspective, an analysis of computer and social architectures is conducted that reinterprets Foucault’s disciplines and political anatomy to establish the notion of politics for a purely technical system. This permits an understanding of system organization as modern technology with application to technical and social systems alike. Connecting to Heidegger’s notions of the enframing (Gestell) and a more primal truth (anfänglicheren Wahrheit), the recognition of politics in differently developing systems then challenges the immutability of contemporary organization. Following this critique of modernity and within the conceptualization of system organization, Derrida’s democracy to come (à venir) is then reformulated more abstractly as organizations to come. Through the integration of the discussed concepts, the framework of Large-Scale Systems Composed of Homogeneous Individuals (LSSCHI) is proposed, problematizing the relationships between individuals, structure, activity, and power within large-scale systems. The LSSCHI framework highlights the conflict of homogenizing system-level objectives and individual heterogeneity, and outlines power relations and mechanisms of control shared across different social and technical systems.
Approximate Memristive In-Memory Hamming Distance Circuit
Check out our latest article:
Mohammad M. A. Taha and Christof Teuscher. Approximate Memristive In-Memory Hamming Distance Circuit. ACM Journal on Emerging Technologies in Computing Systems. 16(2):18, 2020. https://doi.org/10.1145/3371391
Abstract: Hamming Distance (HD) is a popular similarity measure that is used widely in pattern matching applications, DNA sequencing, and binary error-correcting codes. In this article, we extend our previous work to prove that our HD circuit is scalable, tolerant to memristor model variability, and tolerant to device-to-device variation. We showed that the operation of our circuit under non-ideal fabrication conditions changes slightly, decreasing the correct classification rates for the MNIST handwritten digits dataset by <1%. Our circuit’s operation is independent of the memristor model used, as long as the model allows a reverse current. Because we leverage in-memory parallel computing, our circuit is n× faster than other HD circuits, where n is the number of HDs to be computed, and it consumes ≈100× − 1,000× less power compared to other memristive and CMOS HD circuits. Used in a full HD Associative Content Addressable Memory (ACAM), the proposed HD circuit consumes only 2.2% of the total system power. Our state-of-the-art, low-power, and fast HD circuit is relevant for a wide range of applications.
