Associate Professor Tu/e
3 maanden geleden
**Engineering Data and AI Supremacy for the Semiconductor Industry: A Research Agenda**:
In a world that is getting smarter (smart cities, smart cars, etc.) there is a growing demand
for chips (semiconductors) that provide products with intelligence and connectivity, in a
secure way. Several markets (automotive, consumer electronics) are severely impacted by
shortage of chips. NXP is an R&D company that exploits latest technologies (16 nm FF, 7 or
even 5 nm) with their innovations. Time criticality is absolutely key for being successful in
this business, and total quality. That’s why there is a growing need for high performance
compute capacity to run Electronic Design Applications (EDA).
Interestingly, it is expected that there will be nearly 10x the number of connected devices in
existence by 2030. This will pose a huge challenge to companies like NXP. Those connected
cars, and robotics.
Broadly speaking and according to McKinsey, semiconductor companies could benefit by
rethinking their approach in six critical areas: technology leadership, long-term R&D, resilience,
talent, ecosystem capabilities, and greater capacity. At the same time, with greater competition,
semiconductor companies would be wise to increase their efforts to recruit talent, including staff
with expertise in process technology and operations management.
**Broad-Spectrum Collaboration Opportunities**:
Quoting from the same article, three foci for joint research and exploitation emerge:
- Ecosystems’ standardization + sustainability analysis, synthesis, and automated support. “In another type of collaboration, companies may create ecosystems in which one player develops intellectual-property (IP) blocks that many customers can leverage. Arm, for instance, has developed an architecture for a processor that others may license. This strategy decreases costs for all involved. Some companies have also formed strong IP partnerships with academic institutions.”
- Service value-chain analysis and automated sustainability support. “Major semiconductor players also have a long history of joining forces to develop and align their technology blocks, thereby reducing the chance that one organization will create a technology that does not fit into the value chain. Similarly, industry associations can play an important role in providing guidance on the long-term technology road maps, and dedicated semiconductor research institutes, such as Imec, in Belgium, may convene players to collaborate during precompetitive research.”
- Strategic innovation services and data products. “Beyond partnerships, semiconductor companies might want to undertake a programmatic M&A strategy—a serial approach to smaller acquisitions, along a specific theme—as the industry consolidates. If they do so, they might benefit by focusing on acquisitions that would allow them to branch into adjacent areas, open important markets, or add capabilities essential for future growth and for extending their technology leadership. The current scarcity of targets, however, requires potential acquirers to investigate and execute mergers quickly.”
**Research Roadmap**:
be smart, namely (1) self-cognisant or able to automatically adapt to growing data
complexities, e.g., by integrating seamlessly and securely diverse computing and data
environments, spanning from core cloud to edge; (2) self-sustaining, or able to intelligently and
automatic (re-)deployment, mobility, and secure adaptability of services from cloud to edge to
diverse users and contexts; (3) self-restraining, or able to—stemming from stringent needs to
address low resources-consumption and waste reduction—steer resource management while
taking into account the openness and (un-)trustworthiness of the underlying resource
management layers.
Such smart operations are therefore required to interface with all the layers in the computing
continuum plane and will likely be required to learn through the monitoring and management of
dynamic load balancing to optimise energy efficiency and maintaining balanced data traffic and
needs and the underlying infrastructures (e.g., considering varying supply of green energy to
optimise environmental footprint).
As an example of this challenge, a software system offering a functionality built on a
neural-network may only be effectively assessed in a NXP production environment and it is
therefore difficult to test in a traditional software development cycle, because engineers do not
dispose of a training sandbox that is representative of the scale and diversity of production
inputs as well as of the conditions reflected in the above challenges. This triggers a fundamental
challenge for software engineers, namely how can functional correctness and quality-of-service
be guaranteed in large-scale AI-enabled cognitive cloud analytics?
To realize the above vision, and get answers to similar questions as the question above, we
have defined the following key objectives:
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