2023/03/22 by Hartmut Schmidt, José Montes, Schmidt, Hartmut +23 · 2 voices · 2 citations
Computer Science · Engineering · Neuroscience · #Advanced Memory and Neural Computing #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Ferroelectric and Negative Capacitance Devices #Neural and Evolutionary Computing (cs.NE) #Neuroscience and Neural Engineering #cs.ET #cs.NE
paper · pdf · doi:10.48550/arxiv.2303.12359
openalex publication_date 2023/03/22 · arxiv published 2023/03/22 · arxiv updated 2023/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The first-generation of BrainScaleS, also referred to as BrainScaleS-1, is a neuromorphic system for emulating large-scale networks of spiking neurons. Following a "physical modeling" principle, its VLSI circuits are designed to emulate the dynamics of biological examples: analog circuits implement neurons and synapses with time constants that arise from their electronic components' intrinsic properties. It operates in continuous time, with dynamics typically matching an acceleration factor of 10000 compared to the biological regime. A fault-tolerant design allows it to achieve wafer-scale integration despite unavoidable analog variability and component failures. In this paper, we present the commissioning process of a BrainScaleS-1 wafer module, providing a short description of the system's physical components, illustrating the steps taken during its assembly and the measures taken to operate it. Furthermore, we reflect on the system's development process and the lessons learned to conclude with a demonstration of its functionality by emulating a wafer-scale synchronous firing chain, the largest spiking network emulation ran with analog components and individual synapses to date.