1620  Hardware and Software Optimisation

1620.1 Overview

Hardware and software optimization is essential for efficient IoT systems. This topic is covered in four focused chapters that progressively build your understanding from fundamentals through advanced techniques.

TipLearning Path

Start with Optimization Fundamentals to understand trade-offs, then proceed through hardware, software, and fixed-point chapters based on your needs.

1620.2 Chapter Guide

1620.2.1 1. Optimization Fundamentals and Trade-offs

Estimated Time: 25 minutes | Level: Intermediate

Learn the core concepts of IoT optimization including:

  • The four key dimensions: speed, size, power, and energy
  • How application requirements drive optimization priorities
  • Common misconceptions about speed vs. power optimization
  • When to optimize and what to measure first

Key takeaway: For duty-cycled IoT devices, sleep efficiency matters more than processing speed.

1620.2.2 2. Hardware Optimization Strategies

Estimated Time: 30 minutes | Level: Advanced

Explore hardware acceleration options:

  • Processor spectrum: CPU -> DSP -> FPGA -> ASIC
  • ASIC specialization dimensions (instruction set, memory, interconnect)
  • Heterogeneous multicore architectures (ARM big.LITTLE)
  • Memory hierarchy and DMA optimization

Key takeaway: Match hardware capabilities to application requirements considering NRE costs and production volume.

1620.2.3 3. Software Optimization Techniques

Estimated Time: 35 minutes | Level: Advanced

Master software-level optimizations:

  • Compiler flags: -O0, -O2, -O3, -Os trade-offs
  • Code size strategies for flash-constrained devices
  • SIMD vectorization for 4x+ speedup on array operations
  • Function inlining and opportunistic sleeping patterns

Key takeaway: Profile before optimizing - 80% of execution time comes from 20% of code.

1620.2.4 4. Fixed-Point Arithmetic for Embedded Systems

Estimated Time: 25 minutes | Level: Advanced

Implement efficient numeric computation:

  • Qn.m format representation and selection
  • Converting floating-point algorithms to fixed-point
  • Multiplication, division, and overflow handling
  • int8 quantization for edge ML (4x speedup, 4x memory reduction)

Key takeaway: Fixed-point arithmetic offers 4x+ performance at slight precision cost - essential for edge AI.

1620.3 Quick Reference

Topic When to Use Key Benefit
Fundamentals Starting any optimization work Avoid optimizing the wrong thing
Hardware Selecting MCU/accelerator, high-volume products Match hardware to workload
Software Firmware development, code efficiency Faster execution, smaller binaries
Fixed-Point DSP, ML inference, FPU-less processors 4x+ efficiency on integer hardware

1620.4 Prerequisites

Before diving into these optimization chapters, you should be familiar with:

1620.6 What’s Next

Start with Optimization Fundamentals to build a solid foundation, or jump directly to the chapter most relevant to your current project needs.