The Future of Portable AI Applications [Testμ 2026]

At Testμ Conf 2026, Arm’s Prashant Sharma argued that AI makes software portability more complex because applications must operate across combinations of models, frameworks, runtimes, operating systems, hardware accelerators, precision formats, memory limits, and devices. Rather than treating portability as a binary question of whether an application runs, he proposed a developer-defined “quality envelope” that evaluates correctness, performance, efficiency, model quality, and portability according to each target’s needs, such as cloud throughput, laptop responsiveness, phone battery life, or embedded-device memory limits. He emphasized that identical models can produce different outcomes and user experiences across hardware due to variations in operators, runtimes, numerical precision, and memory behavior, while functional tests can miss issues including slow inference, excess memory use, power consumption, and thermal throttling. Sharma advocated automated, continuous testing on representative real, simulated, or hybrid hardware that measures accuracy, latency, memory, power, and sustained thermal performance and reports trade-offs rather than simple pass-or-fail outcomes. He also described a future in which portable runtimes, compilers, and optimization layers hide hardware complexity from developers while validation systems ensure applications meet intended experience thresholds, and he suggested that AI agents may increasingly assist optimization and benchmarking while human critical and systems-level thinking remains important.

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