A 1-Byte Deterministic Acoustic Interface for Ultra-Low-Latency Edge Computing: Hardware-Native Phonetic Encoding via 2KB On-Chip L1 SRAM

Abstract

Contemporary deep neural speech architectures rely heavily on extensive subword tokenizers (e.g., Byte-Pair Encoding) and multi-byte Unicode International Phonetic Alphabet (IPA) representations. These conventional pipelines impose substantial DRAM bus contention, multi-megabyte memory footprints, and non-deterministic inference latencies exceeding 300 ms, rendering them unsuitable for resource-constrained edge microcontrollers (MCUs) and hard real-time reflex control in robotics. This paper proposes AI-IPA, a hardware-native, 1-byte deterministic acoustic interface architecture operating over a strictly closed vocabulary of fewer than 50 symbols (exactly 47 symbols). By mathematically formulating the bio-articulatory mechanics of the Hunminjeongeum matrix, the proposed system completely eliminates external rendering libraries, multi-byte font engines, and off-chip memory dependencies, executing entirely within an allocated 2KB on-chip L1 SRAM address space (0x000–0x800). The architecture specifies deterministic acoustic boundary conditions: low voice onset time (VOT < 15 ms) and vocal fold tenseness for fortis stops (G, D, B, S, J); rapid RMS energy decay (> 24 dB / 10 ms) for glottal stops (Q / ㆆ); the retroflex concatenator (=); syllable-final coda nasalization (N / ㆁ); fundamental monophthong plateaus (a, K, w, o, u, i); extended unitary vowels (x, Y, q, y, W, O); five geometric pitch-contour suprasegmentals (-, /, ~, \, ^); nucleus duration (:); and hardware-level frame delimiters ([, ], _). These acoustic features are directly serialized into a 1-byte stream and interfaced with a hardware Finite State Machine (FSM). Empirical evaluations demonstrate a 99.9% reduction in front-end memory footprint relative to subword tokenizers and an end-to-end reflex latency under 180 ms.