Our high-level synthesis experts Ellie Burns and Michael Fingeroff are talking about AI and machine learning (ML). In episode 4 of their 4 part podcast, they discuss the wide gap between what the best AI applications can perform today versus the human brain and the challenges that hardware designers have in their design flow. They also touch on the clashing requirements of coming up with a generic AI application that can perform many tasks versus applications that perform one task really well.
What you will learn:
- The gaps between AI applications and the human brain. (00:45)
- The Holy Grail of AI: one-shot learning. (01:48)
- The energy consumption of the human brain versus deep neural networks. (02:50)
- The industry’s struggle of creating specific networks versus generic ones. (03:56)
- The resources required by one of the most complex neural networks. (06:08)
- The industry’s challenge of keeping up with the rapid changes in AI architectures. (06:57)
Ms. Burns has over 30 years of experience in the chip design and the EDA industries in various roles of engineering, applications engineering, technical marketing and product management. She is currently the Director of Marketing for the Digital Design and Implementation Solutions Division at Siemens EDA responsible for RTL low-power with PowerPro, high-level synthesis with Catapult and RTL synthesis with Precision and Oasys. Prior to Siemens EDA, Ms. Burns held engineering and marketing positions at Mentor, CoWare, Cadence, Synopsys, Viewlogic, Computervision and Intel. She holds a BSCpE from Oregon State University.
Michael Fingeroff – Host
Michael Fingeroff has worked as an HLS Technologist for the Catapult High-Level Synthesis Platform at Siemens Digital Industries Software since 2002. His areas of interest include Machine Learning, DSP, and high-performance video hardware. Prior to working for Siemens Digital Industries Software, he worked as a hardware design engineer developing real-time broadband video systems. Mike Fingeroff received both his bachelor’s and master’s degrees in electrical engineering from Temple University in 1990 and 1995 respectively.
This podcast features discussions around the importance of AI and ML in today’s industrial world.