If you strip away the hype, classification is the discipline of taking messy inputs and assigning them to meaningful labels, reliably and fast. Do it well and you get real-time fraud flags, safer vehicles, cleaner sensor data, better medical triage. Do it badly and you get alert fatigue, latency spikes, and models that fall apart in the wild. Let’s build the right mental model, then get practical.

What “classification” actually means

1. Task typesBinary: fraud vs not fraud
   Multiclass: gesture A/B/C, species 1..N
   Multilabel: multiple tags can be true at once
2. Modalities
   Images and video frames, audio streams, time-series (ECG, accelerometer, vibration),and tabular logs. Each modality has different pre-processing, windowing, and memory patterns.
3. Output
   Class scores or calibrated probabilities. Calibrated scores are critical when decisions have safety or cost implications.

The end-to-end pipeline

1. Data capture
   Real performance depends on data that matches deployment conditions. Collect from the same sensors, rates, and environments you’ll ship. If you’re working on the edge, this means capturing on the device or dev kit, not just scraping open datasets. Ambient’s SDK adds EdgeSphere, a GUI that lets you capture and label audio and video from real sensors without scripting. That tightens the loop from data to model to field test.
2. Labeling strategy
   -Manual labeling with clear guidelines
   -Weak supervision to bootstrap labels
   -Active learning to surface the most informative samples
   -Continual labeling for drifted data after launch
3. Pre-processing
   -Vision: resize, normalize, sometimes patch or crop on device
   -Audio: frame and window; STFT or filter-bank features for robustness
   -Time-series: detrend, denoise, segment; engineer features like RMS, spectral peaks, crossings keep transform slight weight enough to run where inference runs. If your device has an integrated ADC and sensor interfaces, push the simple transforms there.
4.  Model choices
   -Classical ML for small tabular/time-series signals
   -CNNs or Mobile ViT-style hybrids for vision on constrained devices
   -CRNNs or tiny transformers for audio and IMU sequences
   -Distillation to shrink large teacher models into edge-sized students
5. Training details that matter
   Class imbalance: focal loss, class weights, or informed resampling
   Robustness: noise, compression, illumination, device variance
   Calibration: temperature scaling or isotonic regression if thresholds drive actions
6. Evaluation
   Accuracy is rarely enough. Track per-class precision/recall, ROC and PR-AUC, and most importantly latency percentiles and energy per inference at batch size 1. Tail behavior makes or breaks UX.

Why memory and data movement decide wins

Real devices miss targets because the memory hierarchy cannot feed the model. The two numbers that quietly dominate:

• On-chip SRAM size and bandwidth for activations and weights
• DRAM and DMA behavior under bursty sensor loads

An accelerator or AI-native SoC that keeps more of the working set on chip avoids cache thrash and DRAM stalls, which is why spec-sheet TOPS rarely predicts field performance. Ambient’s chips lean into this by pairing programmable AI cores with on-chip SRAM and ultra-low-power sensor I/O so pre-processing and inference can stay local.

Designing for the edge: constraints and patterns

Always-on budgets
If your product listens or watches continuously, you live inside a micro-watt to milli-watt envelope. Architect a two-stage pipeline: a tiny sentinel model runs all the time; a heavier classifier wakes only on credible triggers. Ultra-low-power ADCs and direct sensor interfaces help you live in that envelope.

Quantization and mixed precision
INT8 is the default; INT4 and mixed precision are attractive if accuracy holds. Plan for quantization-aware training and per-channel scales. Some edge SoCs support 8, 16, and 32-bit paths when accuracy demands it.

Streaming windows
Use ring buffers for IMU and audio. Window sizes and hop lengths should be derived from event physics, not round numbers.

Thermals and sustained performance
Measure steady-state latency and power after 10–15minutes at realistic ambient temperature. Classification that looks fine in the first minute can degrade under heat.

A concrete example: audio glass break vs everything else

• Capture: collect weeks of in-home audio with normal life sounds, plus controlled glass events on multiple mics. Use a GUI tool to label wave forms quickly and export windows and labels.
• Features: 32–64 mel bands, 20–40 ms windows, 10 ms hop, per-channel energy normalization.
• Model: small CRNN with depth wise separable conv; constrain parameters to fit on-chip memory.
• Training: aggressive augmentation for non-glass transients; focal loss to tame imbalance.
• Deployment: sentinel energy-based detector gates the CRNN. Quantize to INT8.Verify p95 latency, false alarms per day, and energy per hour on the target SoC.
• Field loop: capture misfires, relabel, retrain, OTA update.

Tooling and workflow that reduce risk

• Integrated SDK and compiler
  Moving from PyTorch/Keras into embedded inferenceneeds stable conversion, op coverage, and debugging hooks. Ambient’s stackships a full SDK and compiler for its chips so you are not stuck in “toolchainpurgatory,” and the latest release adds a no-code capture and labeling loop viaEdgeSphere.
• Application-first silicon
  If the same device handles pre-processing, inference, and trigger logic with on-device memory, you avoid performance cliffs caused by shuttling data around. Ambient positions GPX10/GPX10 Pro exactly for that end-to-end pattern.

Metrics that actually matter in production

• Class-wise recall for the events you care about most
• False positives per day in real environments
• p95 and p99 latency at batch 1under sensor load
• Joules per decision at steady state
• Drift rate and time-to-relabeled-model in the field

Common failure modes and how to avoid them

• Training–deployment mismatch
  Train on lab microphones, deploy on cheap MEMS mics with different frequency response. Fix with matched capture or explicit response simulation in augmentation.
• Thresholds copied from clean validation
  Re-tune on noisy field data; ship adaptive thresholds if the environment varies by installation.
• Quantization surprises
  Simulate quantization during training and test numerics on real silicon early.
• Operator fallbacks
  If a converter silently routes ops to a slow CPU path, your latency will explode. Verify op coverage on target.
• No plan for drift
  Build in a capture path for long-tail errors from day one, then retrain on the trickle of new data.

Where to go deeper

If you are building always-on classification for wearables, smart home, or industrial sensing, study the device-level pieces that let you keep the entire loop on the edge: programmable AI cores, on-chip SRAM, and ultra-low-power sensor I/O. Ambient’s product and blog pages outline how GPX10 and the newer GPX10 Pro approach exactly this problem space and how the SDK’s EdgeSphere tool accelerates data collection and labeling.