Configurable analog-digital conversion using the neural engineering framework.

Area of Science:

• Mixed-signal integrated circuit design
• Neuromorphic engineering
• Digital signal processing

Background:

• Conventional analog-to-digital converters (ADCs) require high-precision analog circuits, limiting technology portability.
• Previous neurally-inspired ADCs also suffered from technology dependence and precision requirements.
• Mixed-signal integrated circuits rely heavily on efficient ADCs for various applications.

Purpose of the Study:

• To present a novel analog-to-digital converter (ADC) design based on the Neural Engineering Framework (NEF).
• To overcome the technology portability limitations of conventional and prior neurally-inspired ADCs.
• To leverage neuromorphic network parallelism for a scalable and reconfigurable ADC.

Main Methods:

• Implementation of an ADC architecture utilizing the Neural Engineering Framework (NEF).
• Performing a significant portion of the analog-to-digital conversion process within the digital domain.
• Exploiting the inherent parallelism of neuromorphic networks for conversion.

Main Results:

• The proposed NEF-based ADC demonstrates high technology portability by minimizing analog circuit dependency.
• The design achieves significant scalability due to the parallel processing capabilities of neuromorphic networks.
• The ADC offers runtime reconfigurability for sampling rate, resolution, and transfer characteristics.

Conclusions:

• The NEF-based ADC presents a significant advancement in mixed-signal integrated circuit design.
• This approach offers a scalable, technology-portable, and reconfigurable alternative to conventional ADCs.
• Future work may focus on further optimizing performance and exploring diverse applications for this neuromorphic ADC.