What AI acceleration techniques are used for embedded devices

Artificial Intelligence (AI) is no longer confined to data centers. Today, AI is widely used and implemented in edge devices, smartphones, and embedded systems. This has been made possible through hardware and software acceleration methods that work together within embedded systems. It’s now feasible to run small machine-learning models on low-power, resource-constrained microcontroller units without latency or Internet connectivity.

The implementation of AI on microcontrollers has significantly benefited sectors such as wearable technology, industrial automation, and home automation. AI at the edge enables intelligent decision-making in a highly cost-effective, power-efficient, and reliable manner. This is particularly important for devices that process real-time applications in environments with limited or no connectivity.

AI acceleration on embedded devices is primarily geared toward inference rather than training. AI model training typically requires massive amounts of data and extensive processing power, making it impractical for single-microcontroller devices. While small machine-learning (ML) models with limited datasets can be trained on microcontrollers, the process remains too time-consuming to be practical. As a result, AI acceleration in embedded devices focuses primarily on AI inference.

AI inference acceleration on embedded devices has been achieved through hardware and software optimization. Conventional hardware acceleration involved adding more resources until computation times became reasonable. However, with advancements in AI chips, hardware acceleration now focuses on optimizing resources to efficiently run AI models on low-power devices, sometimes with cloud offloading.

Software acceleration has played a crucial role in reducing AI workloads, making it possible to run models without large processors or specialized hardware. With the ideal hardware and software acceleration combination, ML models can now be deployed on microcontrollers like the ESP32, STM32, and Arduino.

In this article, we’ll explore various hardware and software acceleration techniques that have enabled AI implementation on microcontrollers and embedded systems.

Hardware acceleration ethods

Let’s first examine the different hardware acceleration techniques employed for AI inference on embedded devices.

Use of specialized processors

To enable AI inference on embedded devices, specialized processors are integrated onboard or on-chip to offload specific AI computations from the general-purpose microcontroller unit. This involves GPUs for processing graphics, images, video, or parallel computing tasks.

AI inference for audio, video, or communication is often handled by Digital Signal Processors (DSPs). A Neural Processing Unit (NPU) may be integrated into neural networks or ML algorithms. Similarly, a Vision Processing Unit (VPU) can be used for image processing, classification, and object detection tasks.

Some microcontrollers, such as those with ARM Cortex-M4F or M7 cores, include DSP extensions that accelerate mathematical operations commonly used in AI applications.

Use of external AI accelerators

In many embedded devices, external AI accelerators (such as small FPGAs or dedicated AI chips) are connected to a microcontroller to offload AI processing. This typically involves the use of FPGAs (reconfigurable hardware customized for specific AI tasks) or AI ASICs (custom-designed chips that provide the highest performance and efficiency for particular AI algorithms).

Dedicated hardware circuits such as Fast Fourier Transform (FFT) or cryptography can also be employed for specialized tasks. However, adding external accelerators like FPGAs, AI ASICs, or dedicated hardware units increases cost and system complexity.

System-level techniques

Several system-level techniques are employed to enhance hardware acceleration on embedded devices. These techniques focus on optimizing how different components work together to maximize AI performance.

System-on-a-Chip (SoC) design: SoCs integrate multiple components (including the CPU, memory, GPUs, DSPs, I/O interfaces, and specialized processors) onto a single chip for efficient communication. Advanced interconnect architectures like Network-on-Chip (NoC) enable high-bandwidth, low-latency communication between processing units, reducing delays and improving energy efficiency. SoCs often incorporate heterogeneous processing cores, each optimized for specific tasks, allowing workloads to be efficiently distributed.
Memory hierarchy and optimization: Memory bottlenecks can be minimized using cache optimization and Direct Memory Access (DMA) techniques. Multi-level caches, partitioning, and coherence protocols improve data access speeds. At the same time, DMA allows hardware accelerators to access main memory directly without CPU intervention, reducing CPU load and expediting data transfers. Some systems also employ sophisticated memory controllers to manage access, prioritize requests, and optimize data flow.
Interconnect and communication: High-speed interfaces such as PCIe and dedicated interconnects facilitate fast data transfer between system components. Efficient communication protocols help minimize overhead while ensuring reliable data transmission.
Power management: AI-optimized embedded systems often incorporate various power management techniques to enhance efficiency. These include Dynamic Voltage and Frequency Scaling (DVFS), power gating, and thermal monitoring, which help balance performance with power consumption

Software acceleration methods

Various software acceleration techniques enhance AI performance on microcontrollers and embedded systems. While hardware acceleration is essential, software optimization is significant, particularly in resource-constrained embedded environments.

The following are key software acceleration methods:

Parallelization

Parallelization involves breaking down a computational task in AI code into smaller subtasks that can be executed concurrently. Instead of performing AI computations sequentially, multiple steps run simultaneously, significantly reducing execution time. Several techniques enable parallelization:

Multi-threading: The program is divided into multiple threads that run concurrently within the same process. Since threads share the same memory space, communication between them is efficient.
Single instruction, multiple data (SIMD): This technique performs the same operation on multiple data elements simultaneously. Processors with SIMD capabilities can execute a single instruction across a vector of data, making this method highly effective for repetitive tasks such as image and signal processing.
Task parallelism: This method divides a program into independent tasks that can execute concurrently on different processors or cores. Each task performs a different operation on separate data, making data acquisition, processing, and visualization practical.
Data parallelism: Data is split into smaller chunks, with each processor or core handling a different portion in parallel. This is useful for processing large datasets, such as dividing an image into tiles and processing them simultaneously.

While parallel programming boosts efficiency, it’s more complex than sequential computing. Managing threads and processes incurs overhead, which sometimes negates the benefits of parallelism. Certain program parts cannot be parallelized, limiting overall speed improvements.

The effectiveness of parallelization depends heavily on the underlying hardware, including the number of cores, processors, memory, and communication speed.

Memory management

Memory management is crucial for AI performance in embedded systems, which typically have limited RAM and suffer from memory access bottlenecks. Even with hardware acceleration, performance degrades if the processor spends too much time waiting for data. Additionally, minimizing power consumption during memory access is a key challenge in embedded design.

Several software techniques improve memory efficiency:

Minimizing memory reads and writes: Writing optimized code that reduces unnecessary memory access enhances performance.
Efficient memory allocation:
- Compile-time allocation: Memory is allocated statically at compile time, which is useful for applications with fixed memory requirements.
- Dynamic allocation: Memory is allocated at runtime and deallocated when no longer needed.
- Memory pooling: A pre-allocated pool of fixed-size memory blocks reduces the overhead of frequent memory allocations. This is often more efficient than dynamic allocation.
Optimized data structures: Choosing memory-efficient data structures can significantly improve performance. For example, using arrays instead of linked lists for sequential data access minimizes memory overhead.

When combined with hardware optimizations, these software acceleration techniques enable AI models to run efficiently on microcontrollers and embedded systems.

Algorithm optimization

Algorithm optimization is critical for resource-constrained embedded systems, as it reduces the number of operations required to execute a task. These optimizations are hardware-independent and are designed to improve execution speed on any platform.

The first step is choosing the correct algorithm. Analyzing different algorithms’ time and space complexity helps identify those with lower complexity for a given task. It’s essential to consider trade-offs between memory usage, accuracy, and execution time — in some cases, a less accurate algorithm may run significantly faster while still providing acceptable results.

Several techniques can be applied to optimize algorithms:

Loop unrolling: Expanding loops to reduce loop overhead.
Loop fusion: Combining multiple loops into a single loop to minimize overhead.
Loop invariant code motion: Moving unchanging computations outside loops to avoid redundant calculations.
Function inlining: Replacing function calls with direct code execution to eliminate call overhead.
Branch optimization: Minimizing conditional branches or restructuring logic to reduce branching overhead.
Data structure optimization: Selecting a given algorithm’s most efficient data structures.
Integer arithmetic: Using integers instead of floating-point values to enhance processing speed.
Avoiding redundant calculations: Storing intermediate results to prevent unnecessary re-computation.
Using lookup tables: Precomputing results and storing them in lookup tables for faster access.
Recursive solving: Breaking a problem into smaller sub-problems, solving them recursively, and combining the results.
Dynamic programming: Storing results of subproblems to avoid redundant computations in recursive problems.
Approximation techniques: Implementing approximate solutions when precision is not critical, reducing computational overhead.

Hardware-specific software optimizations

These optimizations leverage compiler settings, libraries, and frameworks to take advantage of specific hardware features such as multiple cores, SIMD units, and specialized caches. This minimizes hardware-related bottlenecks like memory access delays and pipeline stalls while improving power efficiency by eliminating unnecessary operations.

Key techniques include:

Compiler optimizations:
- Using instruction scheduling to reorder instructions for efficiency.
- Employing register allocation to store frequently used variables in registers instead of RAM.
- Applying loop optimizations and function inlining to improve execution speed.
- Using Profile-Guided Optimization (PGO), where runtime profiling data helps the compiler make informed optimization decisions.
SIMD instruction use:
- SIMD instructions allow a processor to perform the same operation on multiple data elements simultaneously, which significantly speeds up repetitive computations in tasks such as image processing and video analysis.
- If the processor includes specialized DSP instructions, they should be used whenever applicable.
Cache optimization:
- Ensuring efficient memory access by optimizing cache usage and implementing cache-aware data structures.
Assembly language for performance-critical code:
- Writing performance-critical sections of code in assembly language allows fine-tuned control over hardware execution.
Use application-specific instruction set processors (ASIPs):
- If available, ASIPs should be leveraged to accelerate domain-specific AI tasks.
Hardware abstraction layers (HALs):
- HALs provide software interfaces to hardware accelerators, enabling compatibility and ease of development.

Offloading to the cloud

For complex AI operations, embedded devices can offload processing to a more powerful device at the edge or in the cloud.

In such cases, the microcontroller sends data to the cloud, processes it remotely, and receives the results for further application. This approach is useful when local processing power is insufficient, but it requires network connectivity and may introduce latency.

Model optimization

AI model optimization plays a critical role in software acceleration for embedded devices. Several techniques are commonly used to optimize AI models for efficient deployment on resource-constrained hardware:

Quantization: Reduces the precision of numerical values, such as weights and activations, in the model. Instead of using 32-bit floating-point numbers, models can use 8-bit integers or even lower precision formats. Quantization is applied after training, and while it may lead to some accuracy loss, models can be trained with quantization-aware techniques to minimize its impact.
Pruning: Removes less meaningful connections (weights) or neurons from the neural network. This process identifies and eliminates connections with low magnitude or importance scores, followed by fine-tuning to maintain accuracy. Pruning reduces model size and computational requirements.
Sparsity elimination: Reduces or eliminates zero-valued elements in the model, similar to pruning. However, while pruning removes weights and activations entirely, sparsity elimination optimizes storage and computation by using sparse matrix representations such as Compressed Sparse Row (CSR) or Compressed Sparse Column (CSC) formats. This minimizes storage needs and speeds up computations.
Pre-processing optimization: Enhances the input data before it is fed into the model, improving overall efficiency. This may involve normalization, standardization, or feature extraction to remove noise and ensure consistent input data, ultimately boosting model performance.
Workflow optimization: Focuses on optimizing the entire process of training and deploying the model, rather than just the model itself. This can include:
- Generating synthetic data to expand and diversify the training dataset.
- Early stopping when the model’s validation performance plateaus, reducing unnecessary training cycles.
- Automating model optimization using specialized tools to improve efficiency and deployment.

Conclusion

A combination of hardware and software acceleration techniques has made running AI models on single-microcontroller devices possible. These optimizations are particularly beneficial for AI inference in embedded systems.

While hardware acceleration now focuses on optimizing resources and integrating specialized hardware when needed, software acceleration remains essential in enabling AI on microcontrollers. Key software acceleration strategies include: