The Immutable Tensor Architecture: A Pure Dataflow Approach for Secure, Energy-Efficient AI Inference

The deployment of Large Language Models (LLMs) on consumer edge devices is throttled by the “Memory Wall”—the prohibitive bandwidth and energy cost of fetching gigabytes of model weights from DRAM for every token generated. Current architectures (GPUs, NPUs) treat model weights as mutable software data, incurring massive energy penalties to maintain general-purpose programmability. We propose The Immutable Tensor Architecture (ITA), a paradigm shift that treats model weights not as data, but as physical circuit topology. By encoding parameters directly into the metal interconnects and logic of mature- node ASICs (28nm/40nm), ITA eliminates the memory hierarchy entirely. We present a “Split-Brain” system design where a host CPU manages dynamic KV-cache operations while the ITA ASIC acts as a stateless, ROM-embedded dataflow engine. Logic-level simulation indicates a theoretical upper bound of 4.85× reduction in gate count per multiply-accumulate (243 gates vs 1,180 gates), though conservative estimates accounting for routing congestion suggest a 1.62× system-level reduction is more realistic for first-generation implementations. Physical energy modeling projects a 50× improvement in device-level energy efficiency (4.05 pJ/operation vs 201 pJ/operation), while full system power analysis indicates a 10–15× efficiency gain. FPGA prototype validation shows 1.81× LUT reduction for hardwired constant-coefficient multipliers, empirically validating the direction and lower bound of our efficiency claims. For practical deployment, we demonstrate that TinyLlama-1.1B fits on a single 520 mm2 monolithic die at 28nm, while Llama-2-7B requires an 8-chiplet configuration. The architecture is interface-agnostic, supporting deployment via PCIe (M.2 NVMe), Thunderbolt, or USB. Manufacturing cost analysis shows projected unit costs of $52 (1.1B) and $165 (7B) at 100K+ volume. Device power consumption is 1–3 W, with total system power of 7–12 W including host CPU. ITA raises the barrier to model extraction from $2,000 (software dump) to over $50,000 (specialized reverse-engineering equipment), though we acknowledge side-channel vulnerabilities requiring mitigation.