Edge Model Optimization Faster on Real Silicon

Under Services → TinyML & Edge AI → Edge Model Optimization, WorkSprout shrinks and accelerates models for production edge targets — structured pruning, INT8 quantization, operator fusion, per-layer profiling, and CI gates that keep accuracy and latency inside your SLAs.

Lightweight ML for Embedded Systems TinyML Solutions AI Engine Integration Edge AI Deployment Edge Model Optimization

Lightweight ML for Embedded Systems

Model selection, quantization, and deployment pipelines for microcontrollers and embedded targets — accurate inference within tight memory and power budgets.

TinyML Solutions

End-to-end TinyML programmes — sensor fusion, on-device training workflows, and production firmware integration for real-world edge products.

AI Engine Integration

Integrate TensorFlow Lite, ONNX Runtime, and vendor NPUs into existing firmware and application layers with stable APIs and observability.

Edge AI Deployment

Field deployment of edge AI — OTA update paths, device fleets, monitoring, and rollback strategies for production edge inference.

Edge Model Optimization

Pruning, distillation, INT8 quantization, and kernel tuning so models meet latency and energy targets on target silicon.

OptiSense

Edge Model Optimization

Q4 2025

WorkSprout ML Optimization Team

OptiSense: 4× Faster Vision Inference with 75% Smaller Models on Jetson Production Hardware

WorkSprout optimized OptiSense vision models for Jetson deployment — INT8 quantization with hardware calibration, kernel fusion, and per-layer profiling delivered 4× faster inference, 75% smaller model size, and 97%+ accuracy retained in production.

TensorRT ONNX PyTorch TFLite Nsight GitHub Actions

8 Wk.Profile to Production

4×Inference Speed-up

75%Model Size Cut

100%Client Satisfaction

Model Optimization Capabilities

What WorkSprout delivers for edge model optimization — pruning, quantization, fusion, and benchmark harnesses on target silicon before production cutover.

4× Typical inference speed-up

Structured pruning & distillation

Smaller student models that retain production accuracy.

INT8 & per-channel quantization

Calibration on representative hardware datasets — not lab-only tensors.

Kernel & operator fusion

Graph transforms that cut memory bandwidth and inference time.

Latency profiling per layer

Bottleneck identification on target silicon before cutover.

Energy-per-inference budgets

Power measurements tied to business SLAs, not benchmark leaderboards.

Regression gates in CI

Accuracy and latency thresholds enforced on every model promotion.

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What You Get with Model Optimization

Validated optimized model bundles and repeatable benchmark pipelines — not one-off notebook tweaks — so every release meets latency, size, and accuracy gates.

Optimized INT8 model bundle

Quantized weights, fusion manifest, and checksums for production.

Hardware benchmark harness

Repeatable latency, RAM, and power profiling on target boards.

Layer bottleneck report

Per-operator timing and memory before and after optimization.

Calibration dataset pipeline

Representative field data flows for stable quantization.

CI regression configuration

Accuracy, latency, and size gates in your promotion pipeline.

Optimization runbooks

Repeatable tuning steps your ML team can run each release.

01 — Problem

Why Models Miss Edge SLAs

OptiSense models hit accuracy in PyTorch but missed latency on Jetson — quantization collapsed without calibration, and every new release restarted optimization from scratch.

"Our model was accurate in the lab but too slow and too large on device — and we had no repeatable way to prove a promotion was safe before production."

Models that meet accuracy in PyTorch but miss latency targets on device
Quantization that collapses accuracy without a structured calibration process
No repeatable benchmark harness on real hardware before production cutover
Optimization work repeated from scratch on every new model release
No energy-per-inference budget tied to product power constraints

Hardware Target

NVIDIA Jetson class edge GPU with strict latency and power SLAs.

Model Domain

Production vision models with 97%+ accuracy requirement.

Timeline

8-week optimization: 2 weeks profile, 4 weeks tune, 2 weeks CI cutover.

Deliverables

Optimized bundle, benchmark report, fusion manifest, CI gates, runbooks.

02 — Strategy

Our Optimization Approach

Profile on target hardware first, calibrate quantization on representative data, apply structured pruning and fusion, then enforce regression gates in CI before promotion.

Baseline profile

Layer latency, memory, and power on target silicon documented.

Quantize & prune

INT8 calibration and structured pruning with accuracy gates.

Fuse & validate

Operator fusion and hardware validation before promotion.

03 — Stack

Optimization Toolkit

Toolchains we use to profile, quantize, prune, and validate models on edge hardware.

On-Device ML Runtimes

TensorFlow Lite Micro, ONNX Runtime, CMSIS-NN, and PyTorch export paths sized for MCU flash and SRAM. Applied to Edge Model Optimization engagements.

Jupyter

Tensorflow

ONNX

Tensorflow

Pytorch

Embedded RTOS & MCU

FreeRTOS, Zephyr, STM32, and ESP32 firmware integration with deterministic inference scheduling. Applied to Edge Model Optimization engagements.

ESP32

ARM

Python

ARM

Linux

STM32

Edge Compute & Vision

Jetson, Coral, OpenCV, and GPU-class pipelines for perception workloads at the edge. Applied to Edge Model Optimization engagements.

OpenCV

Docker

Python

Tensorflow

ONNX

Raspberry Pi

Fleet OTA & Observability

MQTT telemetry, Grafana dashboards, Prometheus metrics, and CI/CD for model promotion. Applied to Edge Model Optimization engagements.

Prometheus

GitHub Actions

Docker

Python

Github

InfluxDB

04 — Process

Optimization Delivery Process

Baseline profile → quantize & prune → fuse & tune → validate → CI gates — with accuracy and latency thresholds on real silicon at every stage.

Profile

Baseline latency, memory, and power on production hardware.

Quantize

INT8 calibration with representative datasets.

Prune & distill

Structured pruning and student models where needed.

Fuse & tune

Operator fusion and graph transforms for bandwidth wins.

Validate

Accuracy and latency gates on target silicon.

CI cutover

Regression gates wired into model promotion pipeline.

Tools Used: TensorRTPyTorchONNXNsightGitHub Actions

05 — Milestones

Optimization Snapshots

Visual milestones across a typical edge model optimization engagement — from baseline profiling through production cutover.

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06 — Delivery

Optimization Deliverables

Optimized model bundles, benchmark reports, fusion manifests, and CI regression configs delivered for OptiSense production releases.

Optimized INT8 bundle, benchmark report, and CI gates in production

07 — In Production

Optimized Models Live on Device

How OptiSense runs optimized vision inference on Jetson in the field — latency, power, and accuracy telemetry against production SLAs.

Jetson inference Latency dashboard CI promotions

worksprout.us/portfolio

Live

OptiSense Optimized Inference

4× faster · 75% smaller · 97%+ accuracy · INT8 · TensorRT · CI regression gates

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DeliveredQ4 2025

Duration8 Weeks

ServiceModel Optimization

Speed-up4×

Size cut75%

Satisfaction100%

08 — Impact

Results from Optimization Work

Within 60 days of cutover, OptiSense met latency SLAs, cut model size by 75%, and retained 97%+ field accuracy with automated promotion gates.

4× Faster Inference

Optimized vision models on Jetson met production latency SLAs.

75% Smaller Model Size

INT8 quantization and pruning cut deployment footprint.

97%+ Accuracy Retained

Field accuracy held after optimization on representative data.

Key outcome: A repeatable optimization pipeline with CI regression gates meant new model releases no longer restarted tuning from scratch — promotions shipped with proof of latency, size, and accuracy on Jetson.

09 — Docs

Benchmark & Graph Visuals

Layer profiles, quantization reports, and fusion diagrams from the OptiSense optimization.

Layer Latency Profile

10 — Client Voice

Client Testimonial

★★★★★

"WorkSprout took our Jetson models from too slow to production-ready — INT8 calibration on real data, kernel fusion, and CI gates that block bad promotions. We kept 97%+ accuracy while cutting size by three quarters and inference time by 4×."

11 — Workflow