Tessera: Secure, Near-Line-Rate Weight Streaming for UMA Edge Accelerators
For edge AI systems with shared DRAM between CPU and NPU, Tessera provides a practical DRM solution that avoids the bandwidth and memory overheads of existing defenses.
Tessera introduces an inline, cache-line granularity weight decryption architecture for UMA edge accelerators that achieves 98.4% of theoretical memory bandwidth (1.6% overhead), eliminating the 32x bandwidth penalty of page-level encryption while neutralizing physical DRAM extraction, rogue DMA, and compute hijacking attacks.
Deploying proprietary Deep Neural Networks (DNNs) on commodity edge devices demands hardware-backed Digital Rights Management (DRM) capable of withstanding both software-level and physical adversaries. In Unified Memory Architecture (UMA) systems, the host CPU and Neural Processing Unit (NPU) share physical DRAM, leaving plaintext model weights directly readable by a compromised OS kernel. Existing defenses fail in this constrained setting: trusted execution environments monopolize scarce memory with permanently reserved regions, while full-memory encryption operates at page granularity. This forces the system to fetch massive 4 KB memory pages for sub-page tensor tiles, severely crippling bandwidth. We present Tessera, a reference architecture for inline, cache-line granularity weight decryption on UMA edge accelerators. The design intercepts 64-byte AXI bursts, computing AES-256-CTR keystreams in parallel with DRAM fetches. This streams plaintext directly into isolated NPU SRAM, creating a transient memory footprint confined to the active tile and eliminating the need for permanent memory carve-outs. Measurements across three distinct SoC platforms demonstrate that this parallelization hides cryptographic latency behind standard DRAM fetch times, a condition that holds even under worst-case timing variations. Consequently, Tessera is projected to achieve 98.4\% of the theoretical memory bandwidth ceiling (a mere 1.6\% overhead). Across standard vision and language models, page-level memory encryption suffers up to a 32x bandwidth penalty, whereas Tessera maintains an optimal 1x footprint for all layer geometries. Finally, Tessera neutralizes major UMA-specific attack vectors -- including physical DRAM extraction, rogue DMA, and compute hijacking -- and formally prevents plaintext leakage across sparse tensors.