INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
C’s Dynamic Memory Allocation Assisting AI Model Training  
Dataset AI Embedded Systems use concepts of C’s Dynamic Memory  
Allocation  
Arav Bansal  
Founder & CEO AVAUIRK (OPC) Private Limited  
DOI : https://doi.org/10.51583/IJLTEMAS.2025.1412000116  
Received: 01 January 2026; Accepted: 07 January 2026; Published: 12 January 2026  
ABSTRACT  
Embedded AI devices operate under tight resource constraints (limited RAM, CPU, and power), yet they need  
to run inference efficiently. Dynamic memory allocation and dynamic variables play a crucial role here.  
Embedded systems often use C for AI deployment, so dynamic memory management (malloc, calloc, realloc,  
free) is central for ‘Model Parameter Storage’, ‘Input Buffers for Sensor Data’, ‘Batch Processing and  
Streaming’, ‘Memory Pooling’, ‘Dynamic variables for adaptability’.  
Index Terms— C, Dynamic Memory Allocation, Dynamic Variables, AI Model Training dataset, Embedded  
Systems  
INTRODUCTION  
The rapid evolution of artificial intelligence has led to unprecedented growth in model complexity and dataset  
sizes. Modern AI models, such as large language models (LLMs) and deep neural networks (DNNs), often  
require the handling of billions of parameters and terabytes of training data. This scaling places immense  
pressure on memory management systems, especially when training or deploying models on platforms with  
limited resources. C, as a low-level systems programming language, remains a preferred choice for implementing  
performance-critical components of AI frameworks and embedded systems due to its direct control over memory  
and hardware resources.  
Because of its efficiency, C is frequently used in developing compiler and interpreters. The GNU Compiler  
Collection (GCC) and the Python's CPython interpreter are both implemented in C. Because of C's ability to  
provide a fast speed and portability, both of these compilers/interpreters benefit from parsing the code and  
executing it.  
Dynamic memory allocation in C, facilitated by functions such as malloc, calloc, realloc, and free, allows  
programs to allocate memory at runtime, adapting to varying data sizes and computational demands. This  
flexibility is essential for AI workloads, where the size of input data, intermediate computations, and model  
parameters can change dynamically during training and inference. However, dynamic memory management  
introduces challenges related to performance, fragmentation, security, and determinism, particularly in  
embedded and real-time systems.  
Embedded AI systems, including IoT devices, edge processors, and microcontrollers, operate under stringent  
memory and compute constraints. Efficient memory management in these environments is crucial for enabling  
real-time intelligence and autonomous decision-making without reliance on cloud resources. Frameworks like  
TensorFlow Lite Micro have pioneered memory management strategies that balance flexibility and determinism,  
often leveraging static allocation and custom memory planners to avoid fragmentation and ensure predictable  
behavior.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
This paper aims to provide a comprehensive analysis of dynamic memory allocation and dynamic variables in  
C as they pertain to AI model training datasets and embedded AI systems.  
DESCRIPTION  
Large datasets in AI models often require millions of samples (images, text, sensor data). Static arrays in C (int  
arr[1000];) are insufficient because dataset sizes are not known at compile time. Dynamic allocation (malloc,  
calloc, realloc, free) allows memory to be requested at runtime, adapting to dataset size. Instead of reserving  
huge blocks of memory upfront, allocation can be done in chunks, reducing wasted memory. With this it provides  
both flexibility and improves efficiency which are fundamental needs for AI models. Dynamic memory  
allocation enables:  
Scalability: Handle datasets larger than available memory.  
Adaptability: Adjust to varying input sizes and model architectures.  
Performance: Optimize memory usage, reduce fragmentation, and support parallel training.  
Embedded systems often use ‘C’ for AI deployment, so dynamic memory allocation and dynamic variables play  
a central role in applying:  
1. Model Parameter Storage  
Neural network weights and biases are often too large to store statically.  
Dynamic allocation allows loading only the required parameters into memory at runtime.  
Example: Allocate memory for convolution filters only when a layer is active.  
float *weights = (float *)malloc(num_filters * filter_size * sizeof(float));  
// Use weights during inference  
free(weights);  
2. Input Buffers for Sensor Data  
Embedded AI devices (IoT cameras, wearables) receive variable-sized input streams.  
Dynamic buffers handle unpredictable data sizes (e.g., audio frames, image patches).  
This avoids wasting memory on oversized static arrays.  
3. Batch Processing & Streaming  
Instead of loading the entire dataset, embedded devices process data in mini-batches.  
Dynamic allocation enables creating temporary buffers for each batch, then freeing them to conserve  
RAM.  
4. Dynamic Variables for Adaptability  
Dynamic variables allow the system to adapt to changing conditions:  
Adjusting buffer size based on available memory.  
o
o
Scaling model complexity depending on battery level or CPU load.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XII, December 2025  
Example: A smart camera dynamically reduces image resolution if memory is low.  
5. Memory Pooling  
Embedded AI often uses custom memory pools instead of raw malloc/free to reduce fragmentation.  
Pools allocate a large block once, then subdivide it for tensors, activations, and intermediate results.  
This ensures predictable performance—critical in real-time inference.  
HOW DOES THIS WORK  
To evaluate memory management strategies in AI workloads, we employ a combination of synthetic benchmarks  
and real-world applications:  
Synthetic Benchmarks: Measure allocation/deallocation throughput, latency, and fragmentation under  
controlled conditions using tools like Mimalloc-bench, Google Benchmark, and custom C programs.  
Real-world Applications: Analyze memory usage in AI model training (e.g., LLMs, CNNs) and embedded  
inference engines (e.g., TensorFlow Lite Micro).  
Profiling Tools: Utilize Valgrind, Heaptrack, Intel VTune Profiler, and custom logging to monitor memory  
allocation patterns, leaks, and fragmentation.  
Tools and Profiling Techniques  
Valgrind: Detects memory leaks, invalid accesses, and fragmentation in C programs.  
Heaptrack: Profiles heap memory usage, identifying hotspots and leaks.  
RecordingMicroAllocator (TFLM): Audits memory usage in TensorFlow Lite Micro's tensor arena.  
Custom Logging: Tracks allocation and deallocation events, enabling analysis of memory usage  
patterns.  
Design Patterns and Best Practices  
Memory Pooling: Pre-allocate fixed-size blocks to reduce fragmentation and allocation overhead.  
Custom Allocators: Implement specialized allocators (e.g., TLSF, buddy system) for predictable  
allocation times in real-time systems.  
Static Allocation: Use static buffers for critical data structures in embedded systems to ensure  
determinism.  
Error Handling: Check allocation results for NULL and handle failures gracefully.  
Profiling and Testing: Regularly profile memory usage and test for leaks and fragmentation.  
C Memory Layout and Segmentation  
C programs utilize a well-defined memory model comprising several segments:  
Code Segment: Stores executable instructions.  
Data Segment: Holds global and static variables.  
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INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
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Stack: Manages local variables and function call data, following a Last-In, First-Out (LIFO) structure.  
Heap: Used for dynamic memory allocation, accessed via pointers and managed manually by the  
programmer.  
Dynamic memory allocation occurs in the heap, enabling runtime allocation and deallocation of memory blocks.  
This is in contrast to static allocation, where memory is reserved at compile time and remains fixed throughout  
program execution.  
Dynamic Memory Allocation Functions  
C provides four primary functions for dynamic memory management, defined in <stdlib.h>:  
Table 1: Dynamic Memory Functions  
Function Description  
Usage Example  
malloc  
calloc  
realloc  
free  
Allocates uninitialized space  
int *arr = malloc(10 * sizeof(int));  
int *arr = calloc(10, sizeof(int));  
arr = realloc(arr, 20 * sizeof(int));  
Allocates zero-initialized space  
Resizes previously allocated space  
Deallocates previously allocated space free(arr);  
These functions allow for flexible memory usage, adapting to the needs of the program at runtime.  
Example: Dynamic Array Allocation  
#include <stdio.h>  
#include <stdlib.h>  
int main() {  
int n, i, *ptr, sum = 0;  
printf("Enter number of elements: ");  
scanf("%d", &n);  
ptr = (int*) malloc(n * sizeof(int));  
if(ptr == NULL) {  
printf("Error! memory not allocated.");  
exit(0); }  
printf("Enter elements: ");  
for(i = 0; i < n; ++i) {  
scanf("%d", ptr + i);  
sum += *(ptr + i); }  
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printf("Sum = %d", sum);  
free(ptr);  
return 0; }  
This example demonstrates allocating memory for an array of integers at runtime, processing user input, and  
freeing the memory when done.  
Table 2: Static vs. Dynamic Memory Allocation  
Feature  
Static Allocation  
Compile-time  
Stack  
Dynamic Allocation  
Runtime  
Allocation Time  
Memory Area Used  
Flexibility  
Heap  
Fixed size  
Can change size at runtime  
Deallocation  
Speed  
Automatic (by compiler) Manual (using free)  
Faster  
Slightly slower  
High  
Risk of Fragmentation Low  
Suitability for AI  
Deterministic workloads Variable-sized datasets  
Static allocation is preferred for deterministic, resource-constrained environments, while dynamic allocation  
offers flexibility for handling variable data sizes.  
Key Benefits with use case  
Scientific Computing  
C supports high-performance scientific applications, including numerical simulations and supercomputing tasks.  
Libraries like BLAS are implemented in C for efficient matrix operations in fields like physics and climate  
modelling.  
Real-Time Systems  
C is essential for real-time systems, including aerospace control (usually flight controllers), medical devices, and  
industrial automation. Its deterministic performance provides precise timing within applications, such as flight  
controllers and robotic systems.  
Role in AI Model Training: Handling Large and Variable-sized Datasets  
AI model training involves processing large and often variable-sized datasets, requiring flexible memory  
management. Dynamic memory allocation in C enables:  
Input Data Management: Allocate memory for batches of input data, which may vary in size depending on the  
dataset and batch configuration.  
Model Parameters: Store weights, biases, and other parameters, which can scale to billions in large models.  
Intermediate Computations: Allocate temporary buffers for activations, gradients, and other intermediate  
results during forward and backward passes.  
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Example: Neural Network Forward Pass in C  
#include <stdio.h>  
#include <math.h>  
#include <stdlib.h>  
double sigmoid(double x) { return 1.0 / (1.0 + exp(-x)); }  
int main() {  
int input_size = 3;  
double *weights = (double*) malloc(input_size * sizeof(double));  
double *inputs = (double*) malloc(input_size * sizeof(double));  
double bias = 0.1, output = bias;  
// Initialize weights and inputs  
for (int i = 0; i < input_size; i++) {  
weights[i] = 0.2 + 0.3 * i;  
inputs[i] = 1.0 + i;  
output += weights[i] * inputs[i];}  
output = sigmoid(output);  
printf("Output: %.4f\n", output);  
free(weights);  
free(inputs);  
return 0;}  
This code dynamically allocates memory for weights and inputs, demonstrating how C can manage variable-  
sized data structures in AI computations.  
Benefits of Dynamic Allocation in AI Training  
Flexibility: Adapt to varying dataset sizes and model architectures.  
Efficiency: Allocate only the required memory, reducing waste.  
Scalability: Support large models and datasets by allocating memory as needed.  
Error Handling: Implement robust checks to prevent buffer overflows and allocation failures.  
Benchmarks indicate that dynamic allocation can reduce memory overhead by up to 30% compared to fixed-  
size structures, enhancing performance in large-scale systems.  
Performance Considerations: Speed, Latency, and Throughput  
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Dynamic memory allocation impacts performance in several ways:  
Allocation/Deallocation Overhead: Runtime allocation incurs additional CPU cycles, potentially slowing  
down throughput.  
Fragmentation: Repeated allocations and deallocations can fragment the heap, reducing effective memory  
usage and increasing allocation time.  
Memory Efficiency: Fragmentation, Pooling, and Custom Allocators  
Fragmentation  
Fragmentation occurs when free memory is scattered in small, unusable blocks, leading to inefficient utilization.  
Studies show that up to 30% of usable memory can become fragmented in long-running processes.  
Strategies to Counteract Fragmentation  
Memory Pooling: Pre-allocate fixed-size blocks and recycle them to minimize fragmentation.  
Custom Allocators: Implement allocators tailored to usage patterns (e.g., segregated lists, buddy system).  
Defragmentation Algorithms: Compact memory by rearranging allocations, though this may introduce  
overhead and is less common in C.  
Example: Memory Pool Allocation  
#define POOL_SIZE 256  
char memoryPool[POOL_SIZE];  
Memory pools are particularly effective in embedded systems, reducing allocation overhead and fragmentation.  
Case Study 1 - Embedded AI Systems: IoT, Edge, Microcontrollers  
Embedded AI systems operate under severe resource constraints, necessitating efficient memory management  
strategies:  
Static Allocation: Preferred for deterministic behavior and minimal fragmentation.  
Memory Pooling: Reduces allocation overhead and fragmentation.  
Custom Allocators: Implemented for real-time requirements (e.g., TLSF, Half-Fit).  
Hardware Features: Utilize Memory Protection Units (MPUs) to isolate tasks and prevent unauthorized access.  
Example: TensorFlow Lite Micro on Microcontrollers  
TensorFlow Lite Micro runs on devices with as little as 16KB of RAM, using a pre-allocated tensor arena for  
all memory needs.  
size_t tensor_arena_size = 2048;  
uint8_t tensor_arena[tensor_arena_size];  
tflite::MicroInterpreter interpreter(model, resolver, tensor_arena, tensor_arena_size);  
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No dynamic memory allocation occurs during inference, ensuring deterministic memory usage and avoiding  
fragmentation.  
Real-world Deployments  
Smart Sensors: Deploy lightweight CNNs for real-time anomaly detection on ARM Cortex-M MCUs with tens  
of KB of RAM.  
IoT Devices: Use memory pooling and static allocation to manage sensor data and AI inference buffers.  
Edge AI Processors: Implement custom allocators and memory planners to optimize resource usage.  
TensorFlow Lite Micro and Embedded Frameworks Memory Management  
TensorFlow Lite Micro (TFLM) employs a memory arena divided into head (non-persistent), temporary, and  
tail (persistent) sections.  
Table 3: Memory Arena Structure  
Section  
Purpose  
Shared tensor buffers (non-persistent) GreedyMemoryPlanner  
Resettable chain  
Persistent allocations (model lifetime) Recording API for auditing  
Allocation Strategy  
Head  
Temporary Scoped allocations (method lifetime)  
Tail  
TFLM uses memory planners to optimize buffer reuse, sharing memory between tensors with non-overlapping  
lifetimes. This approach minimizes peak memory usage and ensures efficient operation on resource-constrained  
devices.  
Case Study 2: Real-world Examples and Deployments  
Large Language Model Training  
Training LLMs like GPT-3 requires managing terabytes of data and billions of parameters. Dynamic memory  
allocation is essential for handling variable tensor sizes, optimizer states, and intermediate computations.  
STAlloc: A GPU memory allocator that reduces fragmentation by exploiting spatio-temporal regularity in  
allocation patterns, improving throughput by up to 32.5% and reducing memory waste by up to 43%.  
Embedded Console Memory Management  
The ECDC embedded console uses upfront allocation and fixed-size buffers to avoid runtime fragmentation,  
ensuring predictable memory usage in bare-metal systems.  
Industrial Workloads  
GreenMalloc optimizes allocator configurations for industrial workloads, achieving up to 4.1% reduction in  
average heap usage without loss of runtime efficiency.  
CONCLUSION  
Dynamic memory allocation and dynamic variables in C are indispensable for supporting the flexible, scalable,  
and efficient management of AI model training datasets and AI-enabled embedded systems. The ability to  
allocate memory at runtime enables handling of large and variable-sized datasets, adaptive model architectures,  
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and efficient resource utilization in both cloud and embedded environments. However, dynamic memory  
management introduces challenges related to performance, fragmentation, security, and determinism,  
particularly in resource-constrained and real-time systems.  
Embedded AI systems, such as IoT devices and microcontrollers, require specialized memory management  
strategies to operate within stringent resource limits. Frameworks like TensorFlow Lite Micro exemplify best  
practices by employing static allocation and custom memory planners to ensure deterministic behavior and avoid  
fragmentation. Advanced allocator implementations, including tcmalloc, jemalloc, mimalloc, and TLSF, offer  
optimized solutions for diverse workloads, balancing speed, scalability, and memory efficiency.  
Security remains a paramount concern, with heap corruption and buffer overflows posing significant risks. Tools  
like CAMP, AddressSanitizer, and Valgrind provide robust mechanisms for detecting and mitigating  
vulnerabilities. Profiling and benchmarking tools, such as Heaptrack and Mimalloc-bench, enable developers to  
analyze and optimize memory usage patterns.  
ACKNOWLEDGMENT  
This paper is designed to reflect the importance of ‘C’, in the AI era. While it’s been thought that Python and  
other languages are taking over, but C still stands rock solid in terms of AI Embedded technologies, IoT and plays  
a pivot role in Security working at the core.  
REFERENCES  
Below are the reference sites which I traversed to help build my understanding:  
1. Inventor of C (books, articles) by Dennis MacAlistair Ritchie & Ken Thompson (Dennis Ritchie -  
Wikipedia)  
2. Classic Data Structures by D. Samanta - Classic Data Structures - Google Books  
3. Stanford Education : Scheduling Techniques for Concurrent Systems March 24, 2000 (10)  
4. Dynamic Memory Allocation and Fragmentation – by Nikola Zlatanov (link)  
5. OpenAI website - OpenAI (https://openai.com)  
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