Multiplication by Inference using Classification Trees: A Case-Study Analysis
| Autor: | Roberto Giorgio Rizzo, Andrea Calimera, Valerio Tenace |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Inference 02 engineering and technology Power optimization Multiplier (Fourier analysis) 020204 information systems Logic gate 0202 electrical engineering electronic engineering information engineering Statistical inference 020201 artificial intelligence & image processing Multiplier (economics) Multiplication Arithmetic Boolean function Electronic circuit |
| Zdroj: | ISCAS |
| DOI: | 10.1109/iscas.2018.8351206 |
| Popis: | Inspired by cognitive functions of the human brain, machine learning-driven synthesis flows can map Boolean functions as Classification Trees that work like statistical inference engines. Circuits of this kind infer output values by evaluating the key features of the function learned during the training stage. We propose this idea for arithmetic circuits and, more specifically, for the design of an inferential 8-by-8 bit unsigned multiplier. Using as case-study an error-resilient image blending application, we quantify the most representative figures of merit, also giving comparison against a classical radix-4 multi-level implementation. Experimental results demonstrate the inferential multiplier guarantees 76% average accuracy, 22% less area, and 2× latency reduction that can be used for power optimization. |
| Databáze: | OpenAIRE |
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