Spiking Manifesto
Spiking Manifesto
Speaker Name:
Eugene Izhikevich
Speaker Title:
Founder & CEO
Speaker Organization:
DeepSike Inc.
Start Time:
Thursday, November 12, 2026 - 2:00pm
End Time:
Thursday, November 12, 2026 - 3:00pm
Location:
E2-506 or https://ucsc.zoom.us/j/94971194226?pwd=C3hyuu8iZQrXTaanMkbOjaiScWJVkX.1
Organizer:
Ricardo Sanfelice
Abstract
Computers do almost everything better, faster, and more power-efficiently than the brain. For example, a calculator performs numerical computations more energy-efficiently than any human. Yet modern AI models are a thousand times less efficient than the brain. These models rely on larger and larger artificial neural networks (ANNs) to boost their encoding capacity, requiring GPUs to perform large-scale matrix multiplications. In contrast, the brain’s spiking neural networks (SNNs) exhibit factorially explosive encoding capacity and compute through the polychronization of spikes rather than explicit matrix–vector products, resulting in lower energy requirements. This manifesto proposes a paradigm for framing popular AI models in terms of spiking networks and polychronization, and for interpreting spiking activity as nature’s way of implementing look-up tables. This suggests a path toward converting AI models into a novel class of architectures with much smaller size yet combinatorially large encoding capacity, offering the promise of a thousandfold improvement in performance.
Code is available at https://github.com/izhikevich/SNN
Speaker Bio
Previously founded Brain Corp and led it as CEO for 13 years, growing it to unicorn status as the second-largest mobile robotics company in the world (after Amazon), with 50k commercial robots deployed at Fortune 500 customers, 85% gross margins, and positive EBITDA. Raised $0.25B from top-tier investors, including SoftBank and Qualcomm. Now founded DeepSpike, a fabless AI silicon company applying decades of neuromorphic research to build lookup-table-based accelerators that reduce AI dependence on GPUs and HBM.
