I have a Computer Science degree from Carleton University in Ottawa, Canada which I received in 1984. Today, I am no longer doing software development or engineering work. In the area of Computer Science, there are many talented and hardworking academics who continue to advance the state of the art. One of those is Reiner Hartenstein, Professor of Computer Science, at the Technical University of Kaiserslautern on Germany.
I have never met Dr. Hartenstein, but I became acquainted with his work by chance. I am on Mentor Graphics’ email list for the various events and seminars they offer throughout the year. For some reason, the emails are addressed to Reiner Hartenstein (...Dear Reiner Hartenstein, Please come to….). I have made several attempts to have Mentor rectify the error, but to date I have not been successful.
Since Mentor had pointed out Reiner to me, I decided to do some digging with my favorite search engine and I was quickly led to Dr. Hartenstein. He is a professor at the University of Kaiserslautern, Germany, and was a visiting professor at UC Berkeley. He is a consultant and expert in EDA tools, creator of the language KARL hardware and a pioneer in VHDL and Verilog languages. He has numerous awards including being an IEEE Fellow, and has published 14 books and over 400 technical articles, and has given over 200 lectures worldwide.
Dr. Hartenstein is focused on the important area of Reconfigurable Computing (RC), typically done with FPGAs. The reason the area is important is because of the tremendous energy savings that are possible over traditional von Neumann, CPU-based computing. For some programming tasks, FPGA systems can reduce energy consumption by 3,000 times according to Dr. Hartenstein. One of the challenges for the introduction and adoption of RC is education: there is a lack of tools and methods that can be used in colleges and universities to teach effective RC.
You can learn more about Dr. Hartenstein and his research here:
What are your thoughts on alternatives to traditional computing approaches? Will we ever be effective in programming large numbers of reconfigurable datastream engines? Let me know your thoughts, below.