A reduction of up to seventy-four point six per cent in encapsulation cycles has been achieved by optimising the execution environment surrounding post-quantum cryptography on Arm Cortex-M7 processors. Research focused primarily on improving the core mathematical calculations within these systems, however gains are also possible by better utilising memory and integrating peripheral components. Researchers have identified improvements in how encryption software runs on computer systems beyond simply refining the underlying mathematical calculations.

By optimising memory usage and integrating peripheral components within Arm Cortex-M7 processors, researchers achieved reductions in processing cycles, reaching up to seventy four point six per cent for a specific key encapsulation process called ML-KEM. This optimisation involved reusing publicly available data during computation; previously, the system generated this repeatedly which consumed valuable resources. Researchers at Carleton University and Affiliation: Quantegra Technologies demonstrated performance improvements in post-quantum cryptography running on standard microcontroller hardware.

Their research focused not just on speeding up the complex mathematical calculations within encryption software, a common approach until now, but also on how that software interacts with the computer’s memory and other components. The team reduced processing cycles by up to seventy four point six per cent for ML-KEM, a modern encryption method designed to be secure against future quantum computers, by optimising system resources rather than altering the core cryptographic algorithm itself. Encapsulation and decapsulation, locking and unlocking data with a key, are steps which consume computing power; reducing these steps is key for efficiency.

Optimised execution unlocks post-quantum cryptography on resource constrained devices

A reduction of seventy-four point six per cent in encapsulation cycles resulted from optimising how Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) utilises its execution environment. Repeated public data generation previously hindered performance, particularly for resource constrained devices. Reaching this threshold allows practical application of post-quantum cryptography on Arm Cortex-M7 processors which excessive processing demands hampered during key exchange protocols.

Instruction level tuning and system-wide evaluation unlock these gains, paving the way for wider deployment of secure communication methods against future quantum computing threats. Optimised memory usage alongside traditional instruction tuning yields sharp performance benefits for Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) cryptography. Researchers reduced processing cycles by up to 2.5 per cent when evaluating profiles without auxiliary public state. Deterministic reuse of public data further lowered encapsulation cycle counts by seventy-four point six per cent and decapsulation cycles by fifty-eight point eight percent across all three ML-KEM parameter sets tested on Arm Cortex-M7 processors.

This system level optimisation included tightly coupled memory placement, peripheral integration utilising hardware true random number generators, and varying clock configurations from twenty-four MHz to two hundred and sixteen MHz. Sustained performance under realistic attack conditions or long term key lifecycle management scenarios are not yet demonstrated.

Hardware optimisation delivers immediate gains in encryption efficiency

The advance of quantum computing necessitates a complete overhaul of current encryption methods, forcing cryptographers to seek alternatives durable against these future attacks. Carleton University researchers and Quantegra Technologies demonstrate that squeezing extra performance from existing hardware is equally vital; acknowledging the incremental nature of these gains alongside ongoing algorithmic development remains important. Reducing computational cycles by up to seventy five percent represents a step towards practical deployment for resource-constrained devices like sensors or smartcards. Focusing on memory usage with traditional instruction tuning for Module-Lattice-Based Key Encapsulation Mechanism (ML-KEM) implemented on an Arm Cortex-M7 processor achieved substantial efficiency gains.

The research demonstrated improvements in the speed of ML-KEM cryptography through optimisation of system resources surrounding the core algorithm. These findings suggest that alongside algorithmic advances, optimising execution systems can yield considerable performance increases for post-quantum cryptographic implementations on Arm Cortex-M7 processors.

👉 More information
🗞 System-Level Optimization Beyond Cryptographic Kernels: An ML-KEM Case Study on Arm Cortex-M7
✍️ Mahmoud Abdelhafeez Sayed and Mostafa Taha (Carleton University); Gurp Nijjer (Affiliation: Quantegra Technologies)
🧠 ArXiv: https://arxiv.org/abs/2610.01960

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