Tagged silicon
4 pieces
- · 7 min read
The Cloud Hides Your AI's Real Failures
Four properties of every model are always present and almost never visible: it extrapolates past its data, it discards most of your input, it is memory-bound on a shared bus, and its confidence off-distribution is undefined. A cloud environment hides all four, because a retry, a redeploy, a bigger box, and a human on a dashboard tomorrow each absorb one. Take the second chance away and they stop hiding at once.
- · 18 min read
Sensor Placement Is an Algorithm Decision
On a wearable device, where the sensor physically sits is not a comfort decision, it is the decision that determines whether you need an AI model at all, and whether you ship in 6 months or 18. It is usually made in month 2 by someone who does not work on signals, and by month 9 it has quietly become a year-long AI project nobody planned.
- · 16 min read
The Return-Path Bug That Kills Your Biosignal Front End
On a closed-loop bio device, the instant you turn the actuator on, the signal can vanish to a flat line, and it is not coupling. The current you inject is a loop that has to come home, and it takes the lowest-impedance path, often the exact electrode protecting your front end. It forces a voltage the amplifier cannot reach, the amplifier rails, and your sensor goes blind. The fix is a decision about where the current returns, not a better part.
- · 11 min read
Your AI Is Memory-Bound, Not Compute-Bound (So Tuning the Model Won't Fix Latency)
On fixed hardware, inference latency is set by moving bytes, not by doing math. Most models sit in the memory-bound region with the compute units idle, so the real cost is moving weights and activations across a fixed memory bandwidth. Accuracy, latency, memory, and power are not four goals, they are four views of one thing, the bus, and the worst-case tail is set by contention, not by your model.