The architectural implications of autonomous driving: constraints and acceleration Lin et al., ASPLOS'18 Today’s paper is another example of complementing CPUs with GPUs, FPGAs, and ASICs in order to build a system with the desired performance. In this instance, the challenge is to build an autonomous self-driving car! Architecting autonomous driving systems is particularly challenging … Continue reading The architectural implications of autonomous driving: constraints and acceleration
Author: adriancolyer
Venture Partner with Accel in London, author of 'The Morning Paper'
Darwin: a genomics co-processor provides up to 15,000x acceleration on long read assembly
Darwin: a genomics co-processor provides up to 15,000x acceleration on long read assembly Turakhia et al., ASPLOS'18 With the slow demise of Moore’s law, hardware accelerators are needed to meet the rapidly growing computational requirements of X. For this paper, X = genomics, and genomic data is certainly growing fast: doubling every 7 months and … Continue reading Darwin: a genomics co-processor provides up to 15,000x acceleration on long read assembly
Google workloads for consumer devices: mitigating data movement bottlenecks
Google workloads for consumer devices: mitigating data movement bottlenecks Boroumand et al., ASPLOS'18 What if your mobile device could be twice as fast on common tasks, greatly improving the user experience, while at the same time significantly extending your battery life? This is the feat that the authors of today’s paper pull-off, using a technique … Continue reading Google workloads for consumer devices: mitigating data movement bottlenecks
Securing wireless neurostimulators
Securing wireless neurostimulators Marin et al., CODASPY'18 There’s a lot of thought-provoking material in this paper. The subject is the security of a class of Implantable Medical Devices (IMD) called neurostimulators. These are devices implanted under the skin near the clavicle, and connected directly to the patient’s brain through several leads. They can help to … Continue reading Securing wireless neurostimulators
PrivacyGuide: towards an implementation of the EU GDPR on Internet privacy policy evaluation
PrivacyGuide: Towards an implementation of the EU GDPR on Internet privacy policy evaluation Tesfay et al., IWSPA'18 (Note: the above link takes you to the ACM Digital Library, where the paper should be accessible when accessed from the blog site. If you’re reading this via the email subscription and don’t have ACM DL access, please … Continue reading PrivacyGuide: towards an implementation of the EU GDPR on Internet privacy policy evaluation
End of Term
We've reached end of term again, and The Morning Paper will be taking a two-week break, beginning again on Monday 16th April. I hope you enjoyed the selections from the last three months. So much great research, it's hard to highlight just a few! But in case you missed them, here's a small selection covering … Continue reading End of Term
The surprising creativity of digital evolution
The surprising creativity of digital evolution: A collection of anecdotes from the evolutionary computation and artificial life research communities Lehman et al., arXiv 2018 Today’s paper choice could make you the life and soul of the party with a rich supply of anecdotes from the field of evolutionary computation. I hope you get to go … Continue reading The surprising creativity of digital evolution
Adversarial patch
Adversarial patch Brown, Mané et al., arXiv 2017 Today’s paper choice is short and sweet, but thought provoking nonetheless. To a man with a hammer (sticker), everything looks like a hammer. We’ve seen a number of examples of adversarial attacks on image recognition systems, where the perturbations are designed to be subtle and hard to … Continue reading Adversarial patch
Deep learning scaling is predictable, empirically
Deep learning scaling is predictable, empirically Hestness et al., arXiv, Dec.2017 With thanks to Nathan Benaich for highlighting this paper in his excellent summary of the AI world in 1Q18 This is a really wonderful study with far-reaching implications that could even impact company strategies in some cases. It starts with a simple question: "how … Continue reading Deep learning scaling is predictable, empirically
Anna: A KVS for any scale
Anna: A KVS for any scale Wu et al., ICDE'18 This work comes out of the RISE project at Berkeley, and regular readers of The Morning Paper will be familiar with much of the background. Here’s how Joe Hellerstein puts it in his blog post introducing the work: As researchers, we asked the counter-cultural question: … Continue reading Anna: A KVS for any scale
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