2 citations · 5 across the 11 of their papers we have counts for
12 papers
Inertial Asynchronous Computation
Doruk Efe Gökmen, Michel Fruchart, Dmitrii Zendrikov +3
Computation is the controlled evolution of a state. Asynchronous evolutions, where all parts of the state change in their own time without stopping each other, put this control in…
Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networks
Jonathan Haag, Christian Metzner, Dmitrii Zendrikov +4
On-chip learning is key to scalable and adaptive neuromorphic systems, yet existing training methods are either difficult to implement in hardware or overly restrictive. However, r…
A feedback control optimizer for online and hardware-aware training of Spiking Neural Networks
Matteo Saponati, Chiara De Luca, Giacomo Indiveri +1
Unlike traditional artificial neural networks (ANNs), biological neuronal networks solve complex cognitive tasks with sparse neuronal activity, recurrent connections, and local lea…
Training slow silicon neurons to control extremely fast robots with spiking reinforcement learning
Irene Ambrosini, Ingo Blakowski, Dmitrii Zendrikov +5
Air hockey demands split-second decisions at high puck velocities, a challenge we address with a compact network of spiking neurons running on a mixed-signal analog/digital neuromo…
ElfCore: A 28nm Neural Processor Enabling Dynamic Structured Sparse Training and Online Self-Supervised Learning with Activity-Dependent Weight Update
Zhe Su, Giacomo Indiveri
In this paper, we present ElfCore, a 28nm digital spiking neural network processor tailored for event-driven sensory signal processing. ElfCore is the first to efficiently integrat…
Algorithm-hardware co-design of neuromorphic networks with dual memory pathways
Pengfei Sun, Zhe Su, Jascha Achterberg +3
Spiking neural networks excel at event-driven sensing. Yet, maintaining task-relevant context over long timescales both algorithmically and in hardware, while respecting both tight…
A neuromorphic continuous soil monitoring system for precision irrigation
Mirco Tincani, Khaled Kerouch, Umberto Garlando +4
Sensory processing at the edge requires ultra-low power stand-alone computing technologies. This is particularly true for modern agriculture and precision irrigation systems which…
Waves and symbols in neuromorphic hardware: from analog signal processing to digital computing on the same computational substrate
Dmitrii Zendrikov, Alessio Franci, Giacomo Indiveri
Neural systems use the same underlying computational substrate to carry out analog filtering and signal processing operations, as well as discrete symbol manipulation and digital c…
An Efficient Multicast Addressing Encoding Scheme for Multi-Core Neuromorphic Processors
Zhe Su, Aron Bencsik, Giacomo Indiveri +1
Multi-core neuromorphic processors are becoming increasingly significant due to their energy-efficient local computing and scalable modular architecture, particularly for event-bas…
Genetic Motifs as a Blueprint for Mismatch-Tolerant Neuromorphic Computing
Tommaso Boccato, Dmitrii Zendrikov, Nicola Toschi +1
Mixed-signal implementations of SNNs offer a promising solution to edge computing applications that require low-power and compact embedded processing systems. However, device misma…
EchoSpike Predictive Plasticity: An Online Local Learning Rule for Spiking Neural Networks
Lars Graf, Zhe Su, Giacomo Indiveri
The drive to develop artificial neural networks that efficiently utilize resources has generated significant interest in bio-inspired Spiking Neural Networks (SNNs). These networks…
Neuromorphic dreaming: A pathway to efficient learning in artificial agents
Ingo Blakowski, Dmitrii Zendrikov, Cristiano Capone +1
Achieving energy efficiency in learning is a key challenge for artificial intelligence (AI) computing platforms. Biological systems demonstrate remarkable abilities to learn comple…