The Future of EDA: Hardware, Software, and Systems

Description

This lecture delves into the evolution of the Electronic Design Automation (EDA) industry, exploring the growth of semiconductors, software, and systems. The instructor discusses the shift towards software-defined networking, the impact of subscription models on EDA companies, and the potential convergence of hardware, software, and systems in the Internet of Things era. The lecture also touches on the challenges and opportunities in the EDA market, the role of parallelism in simulation tools, and the business dynamics of EDA companies. Additionally, the discussion covers the importance of architectural lock-ins in the semiconductor industry, the profitability of hardware companies, and the future prospects for EDA in the context of emerging technologies.

Official source

https://mediaspace.epfl.ch/media/0_zsiuc0yv

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Related concepts (12)

Semiconductor

A semiconductor is a material which has an electrical conductivity value falling between that of a conductor, such as copper, and an insulator, such as glass. Its resistivity falls as its temperature rises; metals behave in the opposite way. Its conducting properties may be altered in useful ways by introducing impurities ("doping") into the crystal structure. When two differently doped regions exist in the same crystal, a semiconductor junction is created.

Data parallelism

Data parallelism is parallelization across multiple processors in parallel computing environments. It focuses on distributing the data across different nodes, which operate on the data in parallel. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. It contrasts to task parallelism as another form of parallelism. A data parallel job on an array of n elements can be divided equally among all the processors.

Task parallelism

Task parallelism (also known as function parallelism and control parallelism) is a form of parallelization of computer code across multiple processors in parallel computing environments. Task parallelism focuses on distributing tasks—concurrently performed by processes or threads—across different processors. In contrast to data parallelism which involves running the same task on different components of data, task parallelism is distinguished by running many different tasks at the same time on the same data.

Doping (semiconductor)

In semiconductor production, doping is the intentional introduction of impurities into an intrinsic semiconductor for the purpose of modulating its electrical, optical and structural properties. The doped material is referred to as an extrinsic semiconductor. Small numbers of dopant atoms can change the ability of a semiconductor to conduct electricity. When on the order of one dopant atom is added per 100 million atoms, the doping is said to be low or light.

Semiconductor device

A semiconductor device is an electronic component that relies on the electronic properties of a semiconductor material (primarily silicon, germanium, and gallium arsenide, as well as organic semiconductors) for its function. Its conductivity lies between conductors and insulators. Semiconductor devices have replaced vacuum tubes in most applications. They conduct electric current in the solid state, rather than as free electrons across a vacuum (typically liberated by thermionic emission) or as free electrons and ions through an ionized gas.