Abstract

In the last decade computer systems have experienced a major change in how they are designed, as they have encountered fundamental thermal and power consumption constraints. Moving forward, these constraints are forcing computer architectures (System-on-Chips) to include more power efficient, but less general, hardware accelerators.

It is already common to find heterogeneous SoCs (DSPs, GPUs) on battery operated devices, where heterogeneity provides a means to optimise specific tasks for energy and performance. Current “hot” examples of accelerators are dedicated Neural Processing Units for Deep Neural Network Inference and Training, and FPGAs in data centers.

However, moving beyond these well-funded and established accelerators, what do we need to make usable ubiquitous accelerators? This talk will share our lessons on programming (TornadoVM) and designing future heterogenous SoCs harnessing FPGAs.

Bio

Prof. Mikel Luján is the Arm/Royal Academy of Engineering Research Chair in Computer Systems at the University of Manchester and holds a Royal Society Wolfson Fellowship. He is also the director of the Arm Centre of Excellence at the University of Manchester and held a Royal Society Research Fellowship 2008-2017. In an industrial setting, he is the Chief Scientific Advisor of the spin off company (Amanieu Systems) which is commercialising research on dynamic binary translation for modern Arm processors.

Mikel has authored more than 150 refereed papers and has amassed a rich research experience extending to a range of topics from parallel programming to computer simulation, passing by machine learning and FPGAs. For example, in the last five years he has published papers in FCCM, ACM TOPLAS, IEEE TOC, ICRA, HPCA, PLDI (distinguished paper award 2017), VEE, and ISPASS (best paper award 2017). In other words, he is investigating low power many-core systems considering the full stack. Mikel is well-known for his contributions in speculative parallelisation, transactional memory and dynamic binary instrumentation and translation for Arm.