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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
M.Breternitz (2022). Architectural Predictors using Weightless Neural Networks. Seminar - AMD Research - Advanced Micro Devices  - Oct 28.
Exportar Referência (IEEE) 
M. B. Jr.,  "Architectural Predictors using Weightless Neural Networks", in Seminar - AMD Research - Advanced Micro Devices  - Oct 28, Austin, 2022
Exportar BibTeX 
@misc{jr.2022_1777014697372,
	author = "M.Breternitz",
	title = "Architectural Predictors using Weightless Neural Networks",
	year = "2022",
	howpublished = "Digital",
	url = "https://www.amd.com/en/corporate/research"
}
Exportar RIS 
TY  - CPAPER
TI  - Architectural Predictors using Weightless Neural Networks
T2  - Seminar - AMD Research - Advanced Micro Devices  - Oct 28
AU  - M.Breternitz
PY  - 2022
CY  - Austin
UR  - https://www.amd.com/en/corporate/research
AB  - Weightless neural networks (WNNs) are a type of neural model
which use random access memory (RAM) to determine neuron activation, as opposed to weights and dot products commonly used in modern deep learning approaches. This makes WNN attractive for a class of applications wherein the computational load (and energy requirements) is limited due to device size, cost or battery lifetimes. 
Within processors there is a need for efficient prediction of upcoming events, such as branch prediction, cache tag miss/mit, as well as SOC-wide events that control voltage and frequency for efficient power management.
The reduced requirements in hardware cost and memory size make WNNs well suited for this applications. We describe recent results indicate the suitability for WNN in branch prediction at reduced memory requirements.


ER  -