Titlebook: Embedded Deep Learning; Algorithms, Architec Bert Moons,Daniel Bankman,Marian Verhelst Book 2019 Springer Nature Switzerland AG 2019 Deep L

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oncepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts..978-3-030-07577-4978-3-319-99223-5

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Optimized Hierarchical Cascaded Processing,es in a 100-face recognition example. The chips designed in Chap. . are specifically tuned for usage in a hierarchical setup: networks at reduced precision can be used for simple tasks at a high energy efficiency. The chips designed in Chap. . are good candidates for wake-up stages.

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Circuit Techniques for Approximate Computing, third major contribution of this text. It is a dynamic arithmetic precision scaling method on the circuit-level that enables minimum energy test-time FPNNs and QNNs, as discussed in Chap. .. Chapter . discusses two physically implemented CNN chips that apply this DVAFS technique in real silicon. Bi

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Conclusions, Contributions, and Future Work,feasible given current cellular coverage. Furthermore, real-time applications require low latency connections, which cannot be guaranteed using the current communication infrastructure. Finally, this wireless connection is very inefficient—requiring too much energy per transferred bit for real-time

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Gebrauchstauglichkeit von Medizinproduktenthis chapter lists the challenges associated with the large compute requirements in deep learning and outlines a vision to overcome them. Finally, this chapter gives an overview of my contributions to the field and a general structure of the book.

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Book 2019es on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning..Gives a wide overvie