34 citations · 68 across the 5 of their papers we have counts for
5 papers
The SpiNNaker2 chip: a many-core platform for flexible and scalable brain-inspired computing
Stefan Scholze, Johannes Partzsch, Sebastian Höppner +27
In deep learning, efficiency gets more and more important to compensate for the ongoing growth in model sizes and applications. Neuromorphic hardware has long been advocated as an…
68-Channel Highly-Integrated Neural Signal Processing PSoC with On-Chip Feature Extraction, Compression, and Hardware Accelerators for Neuroprosthetics in 22nm FDSOI
Liyuan Guo, Annika Weiße, Seyed Mohammad Ali Zeinolabedin +13
Multi-channel electrophysiology systems for recording of neuronal activity face significant data throughput limitations, hampering real-time, data-informed experiments. These limit…
A RISC-V MCU with adaptive reverse body bias and ultra-low-power retention mode in 22 nm FD-SOI
Heiner Bauer, Marco Stolba, Stefan Scholze +7
We present a low-power, energy efficient 32-bit RISC-V microprocessor unit (MCU) in 22 nm FD-SOI. It achieves ultra-low leakage,even at high temperatures, by using an adaptive reve…
Hardware Implementation of an OPC UA Server for Industrial Field Devices
Heiner Bauer, Sebastian Höppner, Chris Iatrou +13
Industrial plants suffer from a high degree of complexity and incompatibility in their communication infrastructure, caused by a wild mix of proprietary technologies. This prevents…
The SpiNNaker 2 Processing Element Architecture for Hybrid Digital Neuromorphic Computing
Sebastian Höppner, Yexin Yan, Andreas Dixius +15
This paper introduces the processing element architecture of the second generation SpiNNaker chip, implemented in 22nm FDSOI. On circuit level, the chip features adaptive body bias…