PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 12, 2026Advanced Intelligent Systems0 citationsOpen Access

Activation‐Integrated and Memory‐Assisted Dynamic‐Latch Quantizer for Variation‐Tolerant and Low‐Energy Neuromorphic Computing

View Full Paper
JKJonghyun KoJIJiseong ImRKRyun‐Han Koo

Key Points

  • The aim is to develop a dynamic latch-based ADC that enhances energy efficiency and compactness for neuromorphic computing systems.
  • Developed a dynamic latch-based ADC incorporating charge-trap flash memory cells.
  • Integrated a nonvolatile, electrically programmable threshold parameter in the differential comparator.
  • Conducted performance tests for energy consumption and latency at different technology scales.
  • Achieved a mean differential nonlinearity (DNL) of 0.07 LSB.
  • Demonstrated operation stability up to 100°C.
  • Reduced latency to approximately 26 ps and energy to about 690 aJ at 32 nm scale, outperforming previous designs.

Abstract

Analog‐to‐digital converters (ADCs) remain the dominant area/energy bottleneck in neuromorphic computing (NC) systems. In this work, we propose a dynamic latch–based memory‐assisted ADC that integrates charge‐trap flash (CTF) memory cells to achieve highly compact and energy‐efficient operation. Memory‐assisted dynamic‐latch ADC that embeds a CTF cell in the differential comparator, turning the threshold into a nonvolatile, electrically programable parameter and reusing it to perform in‐ADC nonlinear activation. The 4‐bit ADC tunes its switching point with 10 mV resolution via selective program/erase, exhibits mean DNL = 0.07 LSB, and operates stably up to 100°C. At 500 nm, a unit‐bit conversion requires ∼15 ns and ∼220 fJ per precharge‐and‐evaluate cycle, while scaling to 32 nm reduces the latency to ∼26 ps and the energy to ∼690 aJ. Benchmarks confirm that the proposed ADC achieves superior energy and area efficiency compared to prior designs, establishing it as a compact and viable solution for NC systems.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ko et al. (2026) studied this question.

synapsesocial.com/papers/69b257a296eeacc4fcec66f3https://doi.org/10.1002/aisy.202501499
Ask AI
Helpful
Bookmark
Share
View Full Paper