A nine-transistor current-mode bistable memory cell in 180 nm CMOS is presented with independent tuning of threshold, hysteresis, and gain, shown via schematic simulations for spike-based logic gates and recurrent neural units.
Opportunities for neuromorphic computing algorithms and applications
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
extension 1
citation-polarity summary
representative citing papers
Integrating DVS event data into InterFuser through token fusion yields a driving score of 77.2 and 100% route completion on CARLA benchmarks, indicating improved robustness in dynamic conditions.
Annealing-optimized Ag/HZO memristors demonstrate artificial neurons with TTFS, spike-count, and firing-rate coding modes using minimal circuitry.
Self-organising memristive networks exhibit collective nonlinear dynamics that can support physical learning with parallels to biological plasticity and potential for energy-efficient edge intelligence.
citing papers explorer
-
A Fully Tunable Ultra-Low Power Current-Mode Memory Cell in Standard CMOS Technology
A nine-transistor current-mode bistable memory cell in 180 nm CMOS is presented with independent tuning of threshold, hysteresis, and gain, shown via schematic simulations for spike-based logic gates and recurrent neural units.
-
InterFuserDVS: Event-Enhanced Sensor Fusion for Safe RL-Based Decision Making
Integrating DVS event data into InterFuser through token fusion yields a driving score of 77.2 and 100% route completion on CARLA benchmarks, indicating improved robustness in dynamic conditions.
-
Multiple spiking functionalities in annealing-optimized Ag/Hf$_{0.5}$Zr$_{0.5}$O$_2$-based memristive neurons
Annealing-optimized Ag/HZO memristors demonstrate artificial neurons with TTFS, spike-count, and firing-rate coding modes using minimal circuitry.
-
Self-Organising Memristive Networks as Physical Learning Systems
Self-organising memristive networks exhibit collective nonlinear dynamics that can support physical learning with parallels to biological plasticity and potential for energy-efficient edge intelligence.
- Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations