Future Microprocessors Driven by Dataflow Principles

Subscribers:
344,000
Published on ● Video Link: https://www.youtube.com/watch?v=dC-rrQY5IRs



Duration: 1:26:42
1,375 views
17


Architects and the semiconductor industry as a whole is faced with a unique challenge of improving performance and reducing power consumption of future microprocessors with almost no gain coming from device scaling. The role of architecture is perhaps more important today than it has ever been. Industry has reacted with a short-term goal of domain or application-specific accelerators which do little to improve performance of "general-purpose" applications, suffer from programming challenges, are by design obsoletion-prone, and introduce severe design pressure in maintaining so many designs. Is there a principled way to build future processors that can avoid this trap of adhoc integration? In this talk, I will describe how the underlying principles of explicit dataflow based computation can be applied to both the design of cores and accelerators. Specifically I will highlight a simple observation: a hybrid execution model that allows both sequential and dataflow based execution can be extremely beneficial. When applied to the design of cores, I will show such a hybrid model can increase performance of the state-of-art high performance processors by more than a factor of two while reducing power consumption. Applying these same principles to the design of accelerators, shows we can build a single programmable accelerator that can match the efficiency and performance of "any" domain-specific accelerator.




Other Videos By Microsoft Research


2016-06-13Multi-rate neural networks for efficient acoustic modeling
2016-06-13Unsupervised Latent Faults Detection in Data Centers
2016-06-13System and Toolchain Support for Reliable Intermittent Computing
2016-06-13Gates Foundation Presents: Crucial Areas of Fintech Innovation for the Bottom of the Pyramid
2016-06-13Social Computing Symposium 2016: Harassment, Threats, Trolling Online, Diversity in Gaming is Vital
2016-06-13Bringing Harmony Through AI and Economics
2016-06-13Approximating Integer Programming Problems by Partial Resampling
2016-06-13A Lasserre-Based (1+epsilon)-Approximation for Makespan Scheduling with Precedence Constraints
2016-06-13Towards Understandable Neural Networks for High Level AI Tasks - Part 7
2016-06-13Verasco, a formally verified C static analyzer
2016-06-13Future Microprocessors Driven by Dataflow Principles
2016-06-13Theory and Experiments on the Spontaneous Evolution of Culture
2016-06-13Single-shot error correction with the gauge color code
2016-06-13Robust Spectral Inference for Joint Stochastic Matrix Factorization and Topic Modeling
2016-06-13How Much Information Does a Human Translator Add to the Original and Multi-Source Neural Translation
2016-06-13Opportunities and Challenges in Global Network Cameras
2016-06-13Nature in the City: Changes in Bangalore over Time and Space
2016-06-13Making Small Spaces Feel Large: Practical Illusions in Virtual Reality
2016-06-13Machine Learning as Creative Tool for Designing Real-Time Expressive Interactions
2016-06-13Recent Developments in Combinatorial Optimization
2016-06-13Computational Limits in Statistical Inference: Hidden Cliques and Sum of Squares



Tags:
microsoft research
hardware and devices
data science
computer systems and networking