High throughput architecture for inpainting-based recovery of correlated neural signals
| Autor: | Benjamin Knoop, Heiner Lange, Nils Hulsmeier, Steffen Paul, Jochen Rust, Sebastian Schmale |
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| Rok vydání: | 2016 |
| Předmět: |
Hardware architecture
Signal processing Computer science Quantization (signal processing) 0206 medical engineering Real-time computing Inpainting 020206 networking & telecommunications 02 engineering and technology 020601 biomedical engineering Thresholding 0202 electrical engineering electronic engineering information engineering Throughput (business) Algorithm |
| Zdroj: | EUSIPCO |
| DOI: | 10.1109/eusipco.2016.7760544 |
| Popis: | This paper presents the first hardware architecture for compressing and reconstructing correlated neural signals using structure-based inpainting. This novel methodology is especially important for the realization of implantable neural measurement systems (NMS), which are subject to strict constraints in terms of area and energy consumption. Such an implant only requires a defined controlling of the electrode activity to compress neural data. To achieve an efficient implementation with high throughput at the data recovery, approximately computation of arithmetic operations and elementary functions is proposed by using the logarithmic number system (LNS). Because of the digital quantization effects of the LNS conversions, an inherent thresholding operation arises. The proposed hardware realization significantly reduces the required iteration of inpainting computations. This inherent zero forcing in conjunction with the algorithmic error correction results in a speed-up in terms of neural signal recovery, which results in a throughput of 32 961 parallel reconstructions per second. |
| Databáze: | OpenAIRE |
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