| Autor: |
Daniel Bankman, Marian Verhelst, Bert Moons |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Embedded Deep Learning ISBN: 9783319992228 |
| DOI: |
10.1007/978-3-319-99223-5_3 |
| Popis: |
As discussed in Chap. 1, neural network-based applications are still too costly for them to be embedded on mobile and always-on devices. This chapter discusses hardware aware algorithm-level solutions for this problem. As an introduction to this topic, this chapter gives an overview of existing work in hardware and neural network co-optimizations. Two own contributions in hardware-algorithm optimization are discussed and compared: network quantization either at test- and train-time. The chapter ends with a methodology for designing minimum energy quantized neural networks—networks trained for low-precision fixed-point operation, a second major contribution of this text. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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