Advancing Neuromorphic Computing: Mixed-Signal Design Techniques
  Leveraging Brain Code Units and Fundamental Code Units
Murat Isik Sols Miziev Wiktoria Pawlak Newton Howard
This paper introduces a groundbreaking digital neuromorphic architecture that
innovatively integrates Brain Code Unit (BCU) and Fundamental Code Unit (FCU)
using mixedsignal design methodologies. Leveraging open-source datasets and the
latest advances in materials science, our research focuses on enhancing the
computational efficiency, accuracy, and adaptability of neuromorphic systems.
The core of our approach lies in harmonizing the precision and scalability of
digital systems with the robustness and energy efficiency of analog processing.
Through experimentation, we demonstrate the effectiveness of our system across
various metrics. The BCU achieved an accuracy of 88.0% and a power efficiency
of 20.0 GOP/s/W, while the FCU recorded an accuracy of 86.5% and a power
efficiency of 18.5 GOP/s/W. Our mixed-signal design approach significantly
improved latency and throughput, achieving a latency as low as 0.75 ms and
throughput up to 213 TOP/s. These results firmly establish the potential of our
architecture in neuromorphic computing, providing a solid foundation for future
developments in this domain. Our study underscores the feasibility of
mixedsignal neuromorphic systems and their promise in advancing the field,
particularly in applications requiring high efficiency and adaptability