Embedded Perspectives
Edge AI & Vision
5 articles tagged with Edge AI & Vision.
ADAS Sensor Data Logging: Bandwidth Budgets That Add Up
Every page that tells you an ADAS test vehicle produces terabytes a day states the headline and skips the arithmetic, so you cannot redo it for your own sensor set. This article publishes the arithmetic instead: one formula, every table row derived on the page, a worked eight-hour drive that chains those rows into a sustained write rate, a media count and an offload window, and the five places bandwidth budgets go wrong. Written by the GSAS Micro Systems engineering team in India.
SerDes Camera Links: GMSL, FPD-Link, ASA-ML and A-PHY
Four families carry automotive camera video, and only two of them have public documentation written for buyers. GMSL and FPD-Link are silicon-vendor interface families documented for board designers; ASA-ML and MIPI A-PHY are open specifications with published rate and reach figures and almost no practical buyer-facing writing. This page puts all four side by side with every rate class traced to the page it came from, then covers the part nobody writes: how you record, replay and inject a camera stream on a bench, and why none of it shows up in Wireshark. Written by the GSAS Micro Systems engineering team in India.
Two Arm Keil MDK Webinars This Summer: DevOps and Edge AI for Indian Cortex-M Teams
Arm is running two free live Keil MDK webinars this summer: DevOps with Keil MDK on 23 June and edge AI with ModelNova Fusion Studio on 21 July. What Cortex-M teams in Bengaluru, Hyderabad, Pune, Chennai, Mumbai, and Delhi NCR should know, shared by GSAS, Arm's authorized partner in India for Arm Development Tools.
Capturing I²C/SPI Data to Train Edge AI: A Protocol-Analyzer Workflow for Indian Embedded Teams
Edge AI is only as good as the data it learns from, and for most embedded systems, that data lives on the I²C and SPI buses between the sensors and the MCU. Here is a practical workflow for capturing real, labelled bus data with Total Phase protocol analyzers and turning it into an on-device anomaly-detection model, written for embedded teams in Bengaluru, Pune, Chennai, Hyderabad, Mumbai and Delhi NCR.
Arm DSTREAM Probes and Corstone Reference Subsystems for Indian Teams
A field guide for Indian silicon, automotive, and edge-AI teams choosing between Arm DSTREAM-ST, PT, HT, and XT debug-and-trace probes and adopting Corstone reference subsystems as a pre-silicon starting point.
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