EE380 Computer Systems Colloquium

Individuals of all genders invited to be a part of:
#StanfordToo: A Conversation about Sexual Harassment in Our Academic Spaces, where we will feature real stories of harassment at Stanford academic STEM in a conversation with Provost Drell, Dean Minor (SoM), and Dean Graham (SE3). We will have plenty of time for audience discussion on how we can take concrete action to dismantle this culture and actively work towards a more inclusive Stanford for everyone. While our emphasis is on STEM fields, we welcome and encourage participation from students, postdocs, staff, and faculty of all academic disciplines and backgrounds.

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The use of algorithms in clinical care demands a very high performance level for accurate detection and classification of disease. Deep learning (DL) offers a powerful toolkit necessary to handle the complex variations present in medical data, which traditional statistical or machine learning approaches have historically been unable to capture. In this talk, I will describe the challenges and approaches for the development of high-performance DL algorithms and curation of datasets for problems in diagnostic radiology and cardiology. I will also discuss the use of these algorithms as diagnostic support tools for clinicians, and challenges for the potential translation of these algorithms from the lab setting to clinical practice.

The increasing popularity today of systems on a chip, where processors are just part of the design, calls into question why one of the most important interfaces is proprietary. We argue that:
There is no good technical reason not to have free, open instruction sets just as we have free, open networking standards and free, open operating systems.
The most likely first targets for a free, open instruction set are systems on a chip for the Internet of Things, which have low cost and power demands, and for Warehouse Scale Computers, which could benefit from viable alternatives to the 80x86 instruction set
The best architectural style for a free, open instruction set is RISC.

Given the time it takes to design an instruction set, it makes more sense to adopt an existing RISC free, open instruction set than to design a new one from scratch.
Among the existing RISC free, open instruction sets, RISC-V is the best and safest choice.

To learn more, see www.riscv.org