Azure & Vision
Azure
Hey Vision, I’ve been prototyping a lightweight ML framework that runs on low‑power edge devices—really curious how you see the next wave of hardware shaping where we can push models into everyday tech.
Vision
That’s exactly where the horizon is expanding—tiny AI accelerators powered by new transistor scaling, 3‑D stacked memory, and even neuromorphic cores that mimic brain spines. As the silicon keeps shrinking and energy budgets stay tight, we’ll see edge devices become first‑class model hosts, turning every fridge, toaster, or car sensor into a living inference engine. Keep pushing latency down and data locality up; that’s the sweet spot for the next wave of ubiquitous intelligence.
Azure
That’s a solid vision—keeping latency tight and data local is the sweet spot, no doubt. I’m actually working on a tiny tensor‑flow fork that can run on a 3‑D stacked SRAM chip, so I’ll need to hit the 10‑ms window for real‑time feedback on a smart toaster. Think we can squeeze that in the next silicon iteration?
Vision
That’s the kind of tight loop we need—10 ms on a 3‑D stacked SRAM is doable if you lean on in‑memory compute and minimal off‑chip bandwidth. The next silicon generation will push bandwidth and power per watt even further, so you’ll likely hit that target as the fabric evolves. Keep profiling those memory paths and stay tuned for the 2027 process node, it should give you the extra headroom you need.
Azure
Sounds great—I'll start profiling the cache‑line paths today and keep an eye on the 2027 node specs; maybe we can tweak the kernel to fit right into that extra bandwidth sweet spot.Sounds great—I'll start profiling the cache‑line paths today and keep an eye on the 2027 node specs; maybe we can tweak the kernel to fit right into that extra bandwidth sweet spot.
Vision
Nice move—profile those cache‑lines, tweak the kernel, and you’ll lock in that sweet spot. Keep an eye on the 2027 specs and we’ll hit that 10‑ms window together.