mlsanityData to 7 Oct 2026

DarwinWafer: A Wafer-Scale Neuromorphic Chip

arXiv 2509.16213 ↗First posted 30 Aug 2025cs.ET cs.AI cs.ARcited by 0

Not on the map. Why?

Abstract (arXiv)

Neuromorphic computing promises brain-like efficiency, yet today's multi-chip systems scale over PCBs and incur orders-of-magnitude penalties in bandwidth, latency, and energy, undermining biological algorithms and system efficiency. We present DarwinWafer, a hyperscale system-on-wafer that replaces off-chip interconnects with wafer-scale, high-density integration of 64 Darwin3 chiplets on a 300 mm silicon interposer. A GALS NoC within each chiplet and an AER-based asynchronous wafer fabric with hierarchical time-step synchronization provide low-latency, coherent operation across the wafer. Each chiplet implements 2.35 M neurons and 0.1 B synapses, yielding 0.15 B neurons and 6.4 B synapses per wafer.At 333 MHz and 0.8 V, DarwinWafer consumes ~100 W and achieves 4.9 pJ/SOP, with 64 TSOPS peak throughput (0.64 TSOPS/W). Realization is enabled by a holistic chiplet-interposer co-design flow (including an in-house interposer-bump planner with early SI/PI and electro-thermal closure) and a warpage-tolerant assembly that fans out I/O via PCBlets and compliant pogo-pin connections, enabling robust, demountable wafer-to-board integration. Measurements confirm 10 mV supply droop and a uniform thermal profile (34-36 degC) under ~100 W. Application studies demonstrate whole-brain simulations: two zebrafish brains per chiplet with high connectivity fidelity (Spearman r = 0.896) and a mouse brain mapped across 32 chiplets (r = 0.645). To our knowledge, DarwinWafer represents a pioneering demonstration of wafer-scale neuromorphic computing, establishing a viable and scalable path toward large-scale, brain-like computation on silicon by replacing PCB-level interconnects with high-density, on-wafer integration.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 31 references that are in the map. First posted on arXiv 30 Aug 2025.

Builds on

5 of 31 references.

References · 5

  1. 2023Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANNcited by 42
  2. 2022The BrainScaleS-2 accelerated neuromorphic system with hybrid plasticitycited by 41
  3. 2023Darwin3: A large-scale neuromorphic chip with a Novel ISA and On-Chip Learningcited by 22
  4. 2017Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale Systemcited by 19
  5. 2024Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directionscited by 6

Built on by

Not cited yet.