Question 1

Which countries lead VLSI and FPGA Design Techniques research?

Accepted Answer

By volume, China and the United States publish the most (651 and 406 works in 2022-now).

Question 2

Which institutions lead VLSI and FPGA Design Techniques research?

Accepted Answer

By volume in 2022-now, Fudan University publishes the most VLSI and FPGA Design Techniques research, followed by Chinese Academy of Sciences and Chinese University of Hong Kong.

Question 3

Who are the leading researchers in VLSI and FPGA Design Techniques?

Accepted Answer

The most-cited researchers publishing on VLSI and FPGA Design Techniques include George Davey Smith, Kai You Wang and Christian Szegedy.

Question 4

Where is VLSI and FPGA Design Techniques research done?

Accepted Answer

The largest centres of VLSI and FPGA Design Techniques research in 2022-now are Beijing (China), Shanghai (China), Seoul (South Korea) and Nanjing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Santa Clara and Hsinchu.

Question 5

Where is the best place to study VLSI and FPGA Design Techniques?

Accepted Answer

Among universities, judged by research, Chinese University of Hong Kong, Peking University and Fudan University score highest, combining citation excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

Question 6

Is VLSI and FPGA Design Techniques research growing?

Accepted Answer

Output in 2022-2025 was 30% higher than in 2018-2021, peaking in 2002. The fastest-growing topics are VLSI and FPGA Design Techniques.

Question 7

What are the most cited papers in VLSI and FPGA Design Techniques?

Accepted Answer

The most cited paper in VLSI and FPGA Design Techniques is "Optimization by Simulated Annealing" (Scott Kirkpatrick, C. D. Gelatt, Mario P. Vecchi, 1983), with 45,251 citations. Next come "Multilayer feedforward networks are universal approximators" (21,792) and "Simple statistical gradient-following algorithms for connectionist reinforcement learning" (7,519).