On the Basis of Brain: Neural-Network-Inspired Change in General Purpose Chips

Ekaterina Prytkova () and Simone Vannuccini
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Ekaterina Prytkova: Friedrich Schiller University Jena, Department of Economics and Business Administration

SPRU Working Paper Series from SPRU - Science Policy Research Unit, University of Sussex Business School

Abstract: In this paper, we disentangle the changes that the rise of Artificial Intelligence Technologies (AITs) is inducing in the semiconductor industry. The prevailing von Neumann architecture at the core of the established “intensive” technological trajectory of chip production is currently challenged by the rising difficulty to improve product performance over a growing set of computation tasks. In particular, the challenge is exacerbated by the increasing success of Artificial Neural Networks (ANNs) in application to a set of tasks barely tractable for classical programs. The inefficiency of the von Neumann architecture in the execution of ANN-based solutions opens room for competition and pushes for an adequate response from hardware producers in the form of exploration of new chip architectures and designs. Based on an historical overview of the industry and on collected data, we identify three characteristics of a chip — (i) computing power, (ii) heterogeneity of computation, and (iii) energy efficiency — as focal points of demand interest and simultaneously as directions of product improvement for the semiconductor industry players and consolidate them into a techno– economic trilemma. Pooling together the trilemma and an analysis of the economic forces at work, we construct a simple model formalising the mechanism of demand distribution in the semiconductor industry, stressing in particular the role of its supporting services, the software domain. We conclude deriving two possible scenarios for chip evolution: (i) the emergence of a new dominant design in the form of a “platform chip” comprising heterogeneous cores; (ii) the fragmentation of the semiconductor industry into submarkets with dedicated chips. The convergence toward one of the proposed scenarios is conditional on (i) technological progress along the trilemma’s edges, (ii) advances in the software domain and its compatibility with hardware, (iii) the amount of tasks successfully addressed by this software, (iv) market structure and dynamics.

Keywords: neural network; Artificial Intelligence, technological trajectory; semiconductor industry; hardware; software (search for similar items in EconPapers)
JEL-codes: L63 O31 O33 (search for similar items in EconPapers)
Date: 2020-02
New Economics Papers: this item is included in nep-big, nep-ict, nep-ore and nep-tid
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Related works:
Journal Article: On the basis of brain: neural-network-inspired changes in general-purpose chips (2022)
Working Paper: On the basis of brain: neural-network-inspired changes in general-purpose chips (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:sru:ssewps:2020-01

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