Optimizing Performance in Embedded Systems: Techniques for Working Within Real-World Constraints

CPU cycles are often the most precious resource in an embedded system, and understanding how to use them efficiently can dramatically improve performance, responsiveness, and system stability. This talk focuses on practical, foundational techniques to analyze and optimize CPU utilization in real-world embedded environments.

We begin by examining how to detect whether the CPU, memory, or power subsystem is limiting system performance—with an emphasis on interpreting CPU load, idle time, task scheduling behavior, and profiling data. The session then dives deeper into proven CPU optimization approaches: scheduling improvements, reducing context-switch overhead, identifying hotspots, loop unrolling, caching strategies, data-layout tuning, and other techniques that help squeeze more useful work out of limited hardware.

While the focus is CPU-first, we also cover key memory and power constraints and how they interact with CPU execution, helping developers avoid shifting bottlenecks from one subsystem to another.

To ground these techniques in a modern context, the session closes with an edge AI inference example—deploying a keyword spotting model on an ARM Cortex-M7 using INT8 quantization and CMSIS-NN. This serves not as a deep dive into ML, but as a practical demonstration of how the same CPU optimization principles apply when the workload is a neural network rather than traditional control logic.

This talk is primarily theoretical but supported by selective real-world examples from complex embedded systems. Attendees will walk away with a clear framework for identifying bottlenecks and a practical toolkit of optimization techniques to improve performance on constrained embedded platforms.

What this presentation is about and why it matters

How do you find the real bottleneck in an embedded system, then make the right kind of change without wasting time on the wrong one? Vivek Angoth approaches that problem as a practical tour through performance work on embedded hardware, drawing on his experience at NVIDIA, Apple, Intel, and a brushless motor controller side project. The talk moves between profiling, algorithm choices, memory layout, RTOS behavior, compiler help, and a real embedded AI example on Cortex-M hardware. It is especially useful if you want a grounded way to think about constrained systems, not just isolated tricks.

Who will benefit the most from this presentation

• Embedded firmware engineers working on missed deadlines, high CPU load, or jittery behavior
• Performance-minded developers on Cortex-M or similar constrained MCUs
• Engineers debugging cache, DMA, or peripheral contention issues on real hardware
• Teams bringing ML inference onto microcontrollers and needing a performance frame of reference

What you need to know

You will get the most from this if you are already comfortable with basic embedded development and can follow performance discussions at the system and code level.

• General familiarity with embedded firmware, interrupts, and timing constraints
• Basic understanding of CPU, memory, and peripheral bottlenecks
• Some exposure to profiling or debugging on target hardware
• Helpful, but not required: comfort with RTOS concepts and fixed-point arithmetic

Glossary (terms used in this talk)

• RTOS (Real-Time Operating System): An operating system designed to provide predictable timing behavior for real-time applications.
• Quantization: The process of mapping a high-precision or continuous value to a limited set of representable values. In digital signal processing, quantization introduces approximation error and can affect accuracy and stability.
• Fixed-point: A binary numeric representation with a fixed radix point; the available precision and range are determined by the number of integer and fractional bits.
• Cache (L1/L2/L3): Small, fast memory layers on a CPU organized by latency and capacity. L1 is typically the smallest and fastest, while higher levels are larger and slower.
• RMS (Root Mean Square): A measure of signal energy computed from the square root of the mean of squared sample values; often used as a rough proxy for loudness.
• SIMD (Single-Instruction Multiple-Data): A form of vector processing in which one instruction performs the same operation on multiple data elements simultaneously.
• Amdahl's law: A model for reasoning about speedup when only part of a workload can be parallelized or optimized. The serial fraction places an upper bound on the benefit of adding more cores or acceleration.
• Q format: A fixed-point number representation that uses an implied binary point to encode fractional values in integers. It is common in embedded DSP and ML workloads where floating-point hardware is limited or unavailable.

Toolbox (mentioned in this talk)

• FreeRTOS: A widely used real-time operating system for embedded systems. It is commonly integrated as a library that the application initializes and then starts scheduling tasks.
• ARM Cortex: A family of processor cores widely used in embedded systems, ranging from small microcontrollers to application processors.
• ARM Cortex-M: A family of 32-bit ARM microcontroller cores widely used in embedded systems for real-time control, low-power applications, and general-purpose embedded computing.
• Linux: A family of open-source operating systems widely used as a host environment for embedded development, servers, and development tools. It provides a broad base of runtime services, libraries, and utilities for building and running software.
• STM32Cube: STMicroelectronics' software development ecosystem for STM32 microcontrollers, including peripheral configuration and generated project code.
• STM32: A family of microcontrollers commonly used for embedded control and hardware interfacing. They are often used to drive peripherals such as LEDs, motors, and sensors.

Final thoughts

Practical and systems-oriented, this talk gives you a usable way to think about performance work across the stack, from measurement to code changes to hardware-aware tuning. The real value is a sharper diagnostic lens and a more disciplined way to choose where to spend effort, especially when deadlines, memory limits, or power budgets are tight. Embedded developers, firmware engineers, and anyone pushing ML onto small devices will come away with a better vocabulary for what is actually slowing a system down. It is about making the hardware you already have count.

This overview is AI-generated from the session transcript. Spot an issue? Let us know.