Research Papers On Computer Architecture What Makes Us Human Essay
Aside from energy efficiency, predictability, fault tolerance, accuracy, and security are often at least equally important aspects when designing hardware and software.
Interest in technology and algorithms to help speed up memory access will not go away anytime soon, in my opinion.Since then I have developed embedded code, device drivers and hardware in industry, worked for several years as a performance analyst on large Unix systems, and did my MSc research project on the Wal NUT password capability system.Unusually, I have development and research experience across all layers of a computer system, from analogue hardware up to IPC and applications.This kernel is heir to many of the ideas that were implemented in the Password-Capability System.Focusing on the programmer's view, this paper describes the Password-Capability model and the features of the Walnut Kernel and the design decisions taken in creating it.Still, it won’t stop researches from trying, and I’m pretty sure people are still publishing papers in this area.Unfortunately, a lot of topics that are fundamental to understanding computer architecture are basically dead at this point: Branch Prediction: This field is dead. Single-core optimizations/Instruction-level parallelism: While it would be great to have some innovation into this field again, we’ve unfortunately hit the power wall, and thus we now obtain most of our performance benefits from multi-core.It is a direct evolution of the Monash multiprocessing kernel, developed for the Monash multiprocessor system in 1987, and a major research accomplishment by a long running research group founded and led by the late Professor Chris Wallace.Previous Wal NUT related projects included the addition of a stdio libraries, a compiler port, a network protocol stack design, a shell design and other miscellaneous topics.One of my professors was doing research in , although I haven’t been following the state-of-the-art regarding this topic.Datacenters: While maybe not as “hot” as Deep Learning, this is a hugely important and impactful field that will interest any large tech company that hosts their own data centers (eg. Without industry connections, research in this area is difficult, but any solutions in this area will literally save companies hundreds of millions of dollars.