Toward Deployable Hardware Roots of Trust for Edge AI: Software PUFs and Their Trade-offs
Yilin Li, Farhad Merchant
原文摘要
Deploying AI at the edge increases the need for security features on existing devices. When physical access to the device is available, storing secret keys in non-volatile memory is vulnerable. Physical unclonable functions (PUFs) address this by exploiting the unique yet repeatable characteristics of each device, but conventional implementations require design-stage modifications, making them inapplicable to a vast number of existing devices already manufactured. Recently, software PUFs have emerged as an alternative approach, leveraging analog characteristics exposed through software-only interfaces to derive unique responses, without post-fabrication changes. Since edge AI hardware is bound to long operational lifespans, this offers clear economic and engineering value. Existing research on software PUFs remains fragmented. It lacks a holistic review of the trade-offs among choices of components used as entropy sources and software methods to expose analog characteristics. This gap hinders assessing feasibility, comparing approaches, and identifying open challenges towards a hardware root of trust in existing devices. This paper surveys 37 existing software PUF works and compares them against the requirements of on-device model protection: boot-time versus runtime key derivation, and response latency. It analyzes trade-offs, outlines four key challenges that current software PUFs face for edge AI deployment, and highlights promising directions for overcoming barriers to real-world adoption.
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