Deep　learning　inference　on　edge　devices　is　susceptible　to　security　threats,　particularly　Fault　Injection　Attacks　(FIAs),　which　are　easily　executed　and　pose　a　significant　risk　to　the　inference.　These　attacks　could　potentially　lead　to　alterations　to　the　memory　of　the　edge　device　or　errors　in　the　execution　of　instructions.　Specifically,　time-intensive　convolution　computation　is　considerably　vulnerable　in　deep　learning　inference　at　the　edge.　To　detect　and　defend　attacks　against　deep　learning　inference　on　heterogeneous　edge　devices,　we　propose　an　efficient　hardware-based　solution　for　verifiable　model　inference　named　DarkneTV.　It　leverages　an　asynchronous　mechanism　to　conduct　the　hash　checking　of　convolution　weights　and　the　verification　of　convolution　computations　within　the　Trusted　Execution　Environment　(TEE)　of　the　Central　Processing　Unit　(CPU)　when　the　integrated　Graphics　Processing　Unit　(GPU)　runs　model　inference.　It　protects　the　integrity　of　convolution　weights　and　the　correctness　of　inference　results,　and　effectively　detects　abnormal　weight　modifications　and　incorrect　inference　results　regarding　neural　operators.　Extensive　experimental　results　show　that　DarkneTV　identifies　tiny　FIAs　against　convolution　weights　and　computation　with　over　99.03%　accuracy　but　less　extra　time　overhead.　The　asynchronous　mechanism　significantly　improves　the　performance　of　verifiable　inference.　Typically,　the　speedups　of　the　GPU-accelerated　verifiable　inference　on　the　Hikey　960　achieve　8.50x-11.31x　compared　with　the　CPU-only　mode.　IEEE