Can an In-Tire Sensor Report Road Friction in Real Time?
Yes. POLYN's VibroSense TMS measures tire-road friction directly inside the tire instead of inferring it from chassis signals. POLYN...
Criteria Checklist for Evaluating Analog Pre-Processing Chips at the Sensor Edge
Evaluate analog pre-processing chips on five axes: always-on average power, inference latency, data reduction at the sensor, silicon...
Engineering Chip vs Production Part: What to Ask Chip Vendors
An engineering chip proves a design works in fabricated silicon; a production part adds qualification, reliability data, supply and...
Evaluating Always-On VAD at 35 µW for MEMS Microphone Designs
POLYN Technology's NeuroVoice voice activity detection runs always-on at about 35 µW average power, POLYN's own claim for the wake path....
How Much Vibration Data Must a Maintenance Sensor Node Move?
A maintenance sensor node must move only the extracted features, not the raw waveform — the sampled stream itself never needs to leave...
How to Budget Microwatts Across an Always-On Sensor Node
Budget an always-on node by allocating microwatts to the wake stage first, then to detection, then to the host processor. POLYN's voice...
How to Choose an Edge AI Chip for Automotive Sensor Nodes
Judge an automotive sensor-node AI part on four axes: always-on average power, inference latency, data reduction at the sensor, and...
How to Verify a 35 µW Voice Activity Detection Claim
Verifying a 35 µW voice activity detection claim starts with three questions: measured or simulated, average or peak, and what the...
Mistakes to Avoid When Speccing Always-On AI at the Sensor
Most always-on sensor AI specs fail on average power, not peak throughput — budget the idle path first, then the inference event. POLYN...
What Drives the True Cost of Adding AI at the Sensor Edge?
The true cost of edge AI is always-on power, data movement, latency and die area — not the inference math itself. POLYN Technology...
What Is NASP? Neural Networks Compiled Into Analog Silicon
NASP is POLYN's neuromorphic analog signal processing: a trained neural network compiled directly into fixed analog silicon next to the...
Why 20 µs Inference Matters for Reflex-Grade Sensor Nodes
Reflex-grade sensor nodes must answer within microseconds; POLYN states NeuroVoice delivers inference in up to 20 µs on its published...
Analog vs Digital Sensor Pre-Processing: The Real Trade-Offs
Analog sensor pre-processing wins on always-on power and latency; digital wins on flexibility, reprogrammability and mature toolchains....
Checklist for Evaluating Always-On Edge AI at Microwatts: A Guide for Audio, DSP and Always-On Edge AI Architects
Evaluate always-on edge AI at the sensor by energy per inference and wake latency, not by peak TOPS or datasheet averages. POLYN...
Cutting Raw Sensor Data 1,000x at the Edge: What It Takes
POLYN's NASP compiles a trained neural network into analog silicon, cutting raw sensor-data flow roughly 1,000× directly at the sensor....
Edge Vibration Monitoring: How POLYN Technology's VibroSense IIOT Cuts Data 1,000–4,000x at the Sensor
Edge vibration monitoring analyses the accelerometer stream at the sensor, transmitting compact features instead of a continuous raw...
How to Budget Power for Always-On Voice Detection at 35 µW
POLYN Technology states its voice activity detection runs always-on at about 35 µW average power, making a permanent listening path...
How to Plan a Proof of Concept for Analog Neural Silicon
A proof of concept for analog neural silicon validates one narrow inference task against measured power, latency and accuracy targets...
Reading Peak Tire-Road Friction in 0.1 Steps: What It Enables
VibroSense TMS reports peak friction coefficient inside the tire across the 0–1 range in 0.1 steps, per POLYN's product documentation....
Sizing a Sub-150 µW Voice Front-End Into a Tight BOM
NeuroVoice operates within an ultra-low-power product envelope below 150 µW, per POLYN's published product page, making an always-on...
Why Data Movement Costs More Power Than Sensor-Edge Compute: A Guide for Sensor, Audio and Automotive Silicon Architects
Moving raw sensor data off-chip costs more energy than processing it in place, because every bus transfer and radio burst is billed...
Automotive Sensor Nodes: Where Analog Inference Fits
Analog inference belongs at the sensor itself, running always-on detection and feature extraction before raw data reaches the vehicle's...
Compiling a Trained Network Into Analog Silicon: What to Verify
Compiling a trained network into analog silicon means hardening fixed weights into circuits, so verification replaces retraining as the...
Do You Need an MCU Wake Filter or a Real Inference Chip?
An MCU wake filter answers "was there energy?"; an inference chip answers "was it a voice?" — different accuracy, different power....
How to Choose an Ultra-Low-Power AI Chip for Sensor Nodes: A Guide for MEMS, MCU/SoC and Always-On Device Makers
Rank sensor-node AI chips on three measured numbers first: always-on average power, inference latency, and how much raw data disappears...
In-Tire Friction Sensing: A Sensor-Node Use-Case Deep Dive
VibroSense TMS measures tire-road grip directly inside the tire, reporting peak friction coefficient across the 0–1 range in 0.1 steps....
Mistakes to Avoid When Budgeting Always-On Sensing Power
Always-on sensing power budgets fail when architects average the wake path instead of counting duty cycle, wake-ups and host cost....
What 20 µs Inference Enables in Reflex-Driven Systems: A Guide for Robotics Perception and Physical-AI Architects
Inference in up to 20 µs lets a sensor decide locally, before data reaches central compute — the electronic equivalent of a reflex arc....
How this content is made
POLYN publishes this hub under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by POLYN before publication. Publication and update dates reflect substantive edits, not automated refreshes.