Dynamic Memory Management and Real-Time Heap Monitoring in Embedded Systems

Embedded Dynamic Memory Management: Heap, Risks & Tools

Embedded dynamic memory management is one of the foundational pillars of stability, deterministic performance, and long-term reliability in resource-constrained systems. On ARM Cortex-M microcontrollers — where physical SRAM is rigidly divided into regions like Data Core Coupled Memory (DTCM) for zero-wait-state execution and AXI SRAM for broader storage — runtime memory must be handled with surgical precision. Unlike desktop or cloud environments where virtual memory is practically limitless, embedded systems operate within hard physical boundaries.

Within this hardware framework, the two primary runtime memory areas are the stack and the heap. While the stack operates deterministically via a Last-In, First-Out (LIFO) mechanism for local variables and interrupt contexts, the heap provides an unmanaged pool for dynamic allocation. Effective embedded dynamic memory management demands a deep understanding of malloc(), free(), their failure modes, and the real-time diagnostic tools — such as SEGGER SystemView — needed to monitor them.

1. Embedded Dynamic Memory Management: How the Heap Works

The heap is a dedicated block of memory defined within the system linker script (typically in the .bss section or a custom heap segment) that grows toward high memory addresses or fills a fixed boundary. Dynamic allocation allows systems to share a single memory space among multiple asynchronous tasks whose peak requirements do not overlap in time.

1.1 The Role of malloc()

When an application calls malloc(size_t size), the runtime allocator searches the heap for a contiguous free block matching or exceeding the requested size. It relies on internal bookkeeping — typically a linked list of free blocks — and traverses it using one of three strategies:

• First Fit: Allocates the first block encountered that is large enough. Minimizes search time but accelerates fragmentation near the heap’s start.
• Best Fit: Scans the entire free list for the closest-matching block size, reducing internal waste but increasing search latency.
• Next Fit: Similar to First Fit but resumes from the last allocation position, distributing allocations more evenly across the heap.

Once a suitable block is found, the allocator splits it — returning one part to the application as a pointer, and leaving the remainder on the free list. An invisible metadata header prepended to each block stores its size and allocation status.

1.2 The Role of free()

When a pointer is passed to free(void* ptr), the allocator reads the metadata header immediately preceding it to determine the block size, marks the block as free, and returns it to the free list. Advanced allocators then perform coalescing — merging the newly freed block with adjacent free blocks to rebuild larger contiguous regions and combat fragmentation over time.

2. Heap Memory Risks in Embedded Systems

While dynamic allocation offers flexibility, embedded dynamic memory management introduces serious failure modes in bare-metal and RTOS environments. Unlike general-purpose operating systems, embedded platforms lack garbage collectors or supervisory cleanup mechanisms — making every allocation decision critical.

2.1 Memory Leaks: The Accumulative Failure

A memory leak occurs when malloc() is called but the allocated block is never returned via free(). Because embedded systems have no garbage collector, leaked blocks remain permanently inaccessible. Available heap capacity decreases continuously until a subsequent malloc() call returns NULL. If the application fails to check for NULL, writing to that pointer triggers an immediate HardFault exception on ARM Cortex-M processors.

2.2 Heap Fragmentation: The Structural Trap

Fragmentation occurs even when every malloc() is matched with a free(), if allocations and deallocations happen in random orders and varying sizes. Over time, the heap becomes a mosaic of alternating tiny allocated and free segments. A malloc() requesting a large contiguous block — say, 30KB — may fail even though total free memory exceeds 100KB, because no single continuous 30KB region exists. Fragmentation is notoriously difficult to reproduce in short laboratory tests as it only emerges from long-term runtime patterns.

2.3 Dangling Pointers and Double Frees

A dangling pointer arises when free() is called but the application continues to access that address. The memory may be reclaimed and overwritten by another module, causing silent data corruption. A double free — calling free() twice on the same pointer — corrupts the allocator’s internal free list, causing subsequent allocations to return duplicate pointers or trapping the allocator in an infinite loop.

3. Managing Embedded Memory: Safer Architectural Alternatives

To reduce the risks inherent in embedded dynamic memory management, high-reliability embedded design favors static and pooled memory patterns:

• Static Allocation: Defining memory arrays globally at compile time locks the memory footprint during linking. Edge AI frameworks often use compile-time optimizations — such as the EON Compiler — to allocate a fixed, static Tensor Arena within a specific SRAM block, eliminating all runtime allocation risk.
• Fixed-Block Memory Pools: A memory pool contains a predefined number of identical fixed-size blocks. Because every allocation and deallocation matches the fixed block size, fragmentation is mathematically impossible and allocation time becomes a deterministic O(1) operation — ideal for safety-critical embedded systems.

4. Real-Time Diagnostics for Embedded Dynamic Memory Management via SEGGER SystemView

When dynamic allocation cannot be avoided, real-time diagnostics become essential. SEGGER SystemView is a visual analysis tool that captures and profiles embedded software behavior continuously during full-speed runtime — without halting the CPU, unlike traditional stop-and-inspect debuggers.

4.1 Real-Time Transfer (RTT) Integration

SystemView minimizes execution overhead via SEGGER’s Real-Time Transfer (RTT) technology. A microscopic instrumentation module is embedded into the firmware. Events are formatted into dense binary packets and pushed into a ring buffer in target RAM. A J-Link debug probe reads this buffer over SWD/JTAG while the processor continues executing — requiring no extra pins, imposing less than 1% CPU overhead, and demanding approximately 2KB of ROM and 600 bytes of RAM.

4.2 Heap Monitoring Principles

SystemView monitors heap activity by intercepting allocator calls and logging for each event:

• The timestamp of the transaction, accurate to a single CPU cycle.
• The type of operation: Allocation vs. Deallocation.
• The memory address pointer returned or freed.
• The exact size of the requested memory chunk.

5. GUI Diagnosis and Advanced Troubleshooting

Once data streams from the J-Link probe into the SystemView PC application, the Heap Monitoring Window displays a real-time scope-like graph plotting memory consumption against the system execution timeline.

5.1 Diagnosing a Memory Leak

In a stable application, the heap graph follows a cyclical saw-tooth waveform — memory rises during processing intervals and falls back to a consistent baseline once tasks complete. A persistent upward trend in that baseline signals a leak. Clicking on any step-up in the graph reveals the precise task context and timestamp of the offending allocation, directly narrowing the search for the un-freed pointer.

5.2 Visualizing Fragmentation

SystemView tracks peak allocation limits alongside total unallocated space. When cumulative free memory is large but the maximum allocatable contiguous block is small, SystemView provides empirical evidence of structural fragmentation — helping teams decide when to transition toward static allocation or fixed-block pool models.

Conclusion

Robust embedded dynamic memory management requires far more than pairing each malloc() with a free(). Developers must account for fragmentation, dangling pointers, double frees, and NULL dereferences — all of which can silently degrade or catastrophically crash a system with no OS-level safety net. Wherever possible, static allocation or fixed-block memory pools should replace dynamic heap usage. When dynamic allocation is unavoidable, real-time tools like SEGGER SystemView provide the visibility needed to detect and resolve heap anomalies before they reach production.

The SystemView instrumentation code is available at: https://github.com/SEGGERMicro/SystemView

SEGGER SystemView V4 — ELF File Integration

Released in early 2026, SystemView V4 introduces a paradigm shift in embedded diagnostics: debug information moves off the target firmware and onto the host side, using the ELF file as the bridge. The result is smaller firmware, lower RTT bandwidth, and richer diagnostics — all at once.

What Is the ELF File?

An ELF (Executable and Linkable Format) file is the compiler’s complete record of the firmware — every symbol, every function name, every static object, every global variable, and every log string. It is generated at build time alongside the binary that gets flashed to the MCU. SystemView V4 reads the ELF on the host PC and uses it to fill in details the target no longer has to transmit.

ELF vs BIN — What Is Actually Inside

BIN (Binary)

A .bin file is the raw machine code — nothing more, nothing less. It is a flat, sequential dump of bytes that the MCU can execute directly. It contains:

• Machine instructions (the compiled opcodes)
• Initialized data (global variables with initial values)
• Constants

That is it. No labels, no names, no structure, no metadata whatsoever. If you open a .bin in a hex editor, you see raw bytes with zero context. The MCU does not need context — it just executes byte by byte starting from the reset vector address.

Think of it as the final product — lean, stripped, ready to run.

ELF (Executable and Linkable Format)

An .elf file contains everything the .bin has, plus a large envelope of structured metadata around it. Inside an ELF:

Think of it as the complete engineering drawing — the BIN is just one layer extracted from it.

Key insight: The ELF file travels to the host only — it is never flashed to the MCU. That single design decision is what buys smaller firmware, lower RTT bandwidth, and richer diagnostics simultaneously.

ELF can be programmed to the chip — indirectly

Most professional flash tools —  J-Link, STlink  , STM32CubeProgrammer — accept an ELF file directly as input. You do not have to convert to BIN first. When you do this, the tool:

1. Parses the ELF
2. Extracts only the loadable sections (.text, .data, .rodata — the actual machine code and initialized data)
3. Programs only those bytes to flash
4. Silently discards everything else — the symbol table, DWARF debug info, log strings, resource names

So the ELF is the input to the programmer, but what lands on the chip is identical to what a BIN would produce. The metadata never reaches the flash physically.

Feature 1: ELF File Integration

The new version of SystemView introduces support for adding the firmware ELF file directly to a project. This provides SystemView with crucial information without requiring any modification of the target source code, simplifying both recording and analysis of system behavior.

Feature 2: Print ELF — Log Strings Off the Target

With the Print ELF feature, log messages no longer need to be included in the firmware image. Instead, the target application records only a numeric ID, while SystemView retrieves the corresponding string from the ELF file on the host side.

At build time, each unique log string is assigned a numeric ID and recorded in an ELF section that ships alongside the binary but is never flashed to the MCU. At runtime, the target sends only the 4-byte ID over RTT. SystemView on the host matches the ID against the ELF and displays the full string in the timeline — two simultaneous wins:

• ROM savings: Tens of kilobytes recovered on flash-constrained devices like STM32L4 or Nordic nRF52.
• RTT bandwidth savings: A 4-byte ID instead of a full string dramatically reduces channel load, critical when the same debug channel also carries high-frequency sensor telemetry.

Feature 3: Automatic Resource Naming

Names of system resources — queues, mutexes, tasks, mailboxes — are now extracted directly from the ELF symbol table. SystemView displays resource names out of the box with zero extra registration code in firmware. Instead of opaque IDs like Task_7 or Mutex_3, developers immediately see names like g_flash_write_mutex or adc_dma_complete_sem in the timeline — making race conditions, priority inversions, and resource conflicts immediately identifiable.

RTOS and Platform Compatibility

• embOS (enabled by default since v4.12a)
• FreeRTOS
• Zephyr
• NuttX
• Eclipse ThreadX / Azure RTOS (since v6.4)
• uC/OS-III and Micrium OS Kernel
• Bare-metal (no OS) — interrupt activity and user events

V4 ELF Features — Before vs. After

RTT Overhead (unchanged in V4)

• ROM footprint: RTT + SystemView modules combined < 2KB
• RAM footprint: ~600 bytes for continuous recording
• CPU overhead: < 1% at 10,000 events/second (200 MHz Cortex-M4)
• Extra pins required: None — uses existing SWD/JTAG debug interface

Where to Get SystemView V4

• Download: https://www.segger.com/downloads/systemview/
• Target source & ELF macros: https://github.com/SEGGERMicro/SystemView
• User guide: https://doc.segger.com/UM08027_SystemView.html

SystemView operates by integrating a small software module, containing SystemView and RTT, into the target system. The instrumentation code is available at: https://github.com/SEGGERMicro/SystemView. This module collects event data, formats it, and passes it to the RTT module

SystemView File Structure

What You Download

From https://github.com/SEGGERMicro/SystemView you get:

SystemView/
│
├── SEGGER/
│   ├── SEGGER_RTT.c
│   ├── SEGGER_RTT.h
│   ├── SEGGER_RTT_Conf.h
│   ├── SEGGER_SYSVIEW.c
│   ├── SEGGER_SYSVIEW.h
│   ├── SEGGER_SYSVIEW_Conf.h
│   └── SEGGER_SYSVIEW_Int.h
│
├── Sample/
│   ├── FreeRTOS/
│   │   ├── SEGGER_SYSVIEW_FreeRTOS.c
│   │   └── SEGGER_SYSVIEW_FreeRTOS.h
│   ├── embOS/
│   ├── Zephyr/
│   └── NoOS/
│       └── SEGGER_SYSVIEW_NoOS.c
│
└── Description/
    └── SYSVIEW_FreeRTOS.txt   ← tells the PC app about your RTOS tasks

What Each File Does

Core files — always required

The Practical Takeaway

If you had a working V3 integration, migrating to V4 is essentially:

1. Replace the SEGGER source files with the V4 versions
2. Point the PC application to your firmware.elf
3. Done — automatic naming and Print ELF work immediately with no structural changes to your project

What V4 Actually Changes for the Developer

1. Less ROM consumed on the target

In V3, every log string you wrote — "Task started", "Buffer overflow detected", "Sensor read complete" — had to live permanently in your flash memory. On a device with 256KB or 512KB of flash, a large application with hundreds of log messages could lose tens of kilobytes just to debug strings that serve no purpose in production.

In V4 those strings never touch the flash at all. You write the same log call in your code, but only a tiny numeric ID gets stored and transmitted. The full string stays in the ELF on your PC.

Real benefit: More flash available for your actual application code, without having to choose between good logging and tight memory budgets.

2. Less time spent on instrumentation boilerplate

In V3, every RTOS resource you created — every task, queue, mutex, semaphore — had to be manually registered with SystemView by name. That meant extra lines of code in your firmware just to make the debug tool readable:

SEGGER_SYSVIEW_SendTaskInfo(&taskInfo);  // had to do this for every single task

In V4 you do none of that. SystemView reads the names directly from the ELF. The timeline shows g_sensor_task and g_uart_mutex automatically from the moment you start recording.

Real benefit: Less instrumentation code to write, maintain, and remember to update when you rename things.

3. Faster and cleaner RTT channel

In V3, full strings were transmitted over RTT at runtime. On systems where the RTT channel is also carrying sensor telemetry, ADC data, or other high-frequency events, the string traffic added noise and increased the risk of buffer overflows and lost events.

In V4 only a 4-byte ID travels over RTT instead of a 30-byte string. The channel is quieter and faster.

Real benefit: Fewer dropped events, more reliable recordings, especially on high-activity systems.

4. No firmware changes required to get rich diagnostics

In V3, if you wanted meaningful names and messages in the timeline, you had to instrument your code specifically for SystemView. A new team member picking up the project had to understand the SystemView API just to get readable output.

In V4 you point SystemView at the ELF once and everything is already there — names, messages, resource identifiers — pulled automatically from the build output your compiler already generates.

Real benefit: Lower barrier to entry. A developer unfamiliar with SystemView gets a fully readable timeline on day one without touching the firmware.

5. Production-friendly debug workflow

In V3, shipping a production build meant stripping out all the SystemView instrumentation strings manually or using conditional compilation flags, otherwise you were wasting flash on debug data.

In V4 because the strings never lived in the firmware to begin with, your debug build and production build are effectively the same binary from a flash footprint perspective. There is nothing to strip out.

Real benefit: Cleaner build system, no risk of accidentally shipping a binary bloated with debug strings.

Summary Table

The core advantage is simple: V4 gives you more diagnostic information while asking less of your target hardware and less of your development time. The ELF already exists as a byproduct of your build — V4 simply puts it to work.

in this Blog we can also mention J-scope :

SEGGER J-Scope is a free PC tool that ships with every J-Link package and turns your debug probe into a real-time oscilloscope for your firmware. Point it at the compiled .out / .elf file, pick any global variables from the symbol table, set a sample rate, and J-Scope plots them as live time-series curves while the chip runs at full speed. It works with any Cortex-M, RISC-V, or Renesas RX target that J-Link supports — no IDE integration required, just a J-Link probe and the symbol file from your last build.

What makes it remarkable is the zero firmware cost. J-Scope reads variables directly from chip RAM through the SWD or JTAG debug-access port (DAP) — the same wire that flashes the device. The target CPU isn’t aware it’s being sampled: no ring buffer, no RTT transport, no instrumentation code, no extra cycles burned. Just declare your variable as volatile so the compiler doesn’t optimise it into a register, and J-Scope can sample it at tens to hundreds of kilohertz with literally zero impact on real-time behaviour. For tuning control loops, watching state machines, validating filter outputs, or any debugging task where “what is this value doing over time?” is the question, J-Scope is the lightest possible answer — and unlike a logic analyzer or scope, it works on variables that never leave the chip.

For Heap monitoring who is Better:

The practical efficiency story

On a quiet system (e.g. steady-state firmware doing occasional heap activity):

• J-Scope wins decisively. Zero target overhead, ~2 KB/s SWD traffic, perfect for a 24-hour soak test where you just want to see if the curve drifts down.

On a busy system (e.g. NORA during Wi-Fi handshake with hundreds of allocations per second):

• SystemView gives you every alloc/free with a caller stack and timeline. J-Scope just sees the aggregate “free dropped 4 KB then bounced back”. For finding a leak, SystemView is more efficient per investigator-hour. For monitoring whether one exists, J-Scope is more efficient per CPU cycle.