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Provides a brief introduction to three distributed learning techniques that have recently been developed: lossy communication compression, asynchronous communication, and decentralized communication. These have significant impact on the work in both the system and machine learning and mathematical optimization communities.

Shows how specialized hardware accelerators can provide an answer to the compute stagnation problem and be helpful in reducing data movement bottlenecks by placing them in the right location within the computer architecture. The focus of the book is on Field Programmable Gate Arrays.