AI has outgrown general-purpose compute — yet we’re still stuck in a world dominated by closed, GPU-heavy ecosystems, both in common-awareness and practice.
This session takes a systems-level look at AI accelerators: what they are, basic principles about how they work, and why open source efforts around them matter more than ever.
We’ll cover:
The rise of specialized hardware in the AI stack—and why CPUs and GPUs alone don’t cut it
What makes an AI accelerator, including:
Compute paradigms
Memory models
Dataflow architectures
Specialization around the particulars of an AI model
And more
A reality check on the state of open source hardware and tooling for AI
Why existing open-source efforts mostly build on proprietary hardware—and why building open from the ground up is still a missing piece
The rare, often hobbyist, efforts toward replicating or reimagining open AI hardware
Why the NVIDIA monopoly isn’t just a market issue—it’s a bottleneck for innovation and autonomy
The need for open, auditable, community-driven alternatives in AI acceleration
Emerging efforts, gaps, and future directions worth watching (or contributing to)
To cite a few;
Neuromorphic Hardware
Optical Computation
Alternative Fabrication methodologies
Whether you're an ML engineer, systems nerd, or just curious about what powers the models behind the curtain, this talk aims to offer a deeper technical and ecosystem-level understanding of where we are—and where we need to go.
the importance of specialized hardware in the AI landscape
looking beyond GPUs and the NVIDIA monopoly
an understanding of the basic principles behind AI accelerators
the dismal state of open hardware for AI acceleration
the criticality for open and transparent hardware to come up