CPU-GPU　integrated　edge　devices　and　deep　learning　algorithms　have　received　significant　progress　in　recent　years,　leading　to　increasingly　widespread　application　of　edge　intelligence.　However,　deep　learning　inference　on　these　edge　devices　is　vulnerable　to　Fault　Injection　Attacks　(FIAs)　that　can　modify　device　memory　or　execute　instructions　with　errors.　We　propose　DarkneTF,　a　Fault-Tolerant　(FT)　deep　learning　inference　framework　for　CPU-GPU　integrated　edge　devices,　to　ensure　the　correctness　of　model　inference　results　by　detecting　the　threat　of　FIAs.　DarkneTF　introduces　algorithm-based　verification　to　implement　the　FT　deep　learning　inference.　The　verification　process　involves　verifying　the　integrity　of　model　weights　and　validating　the　correctness　of　time-intensive　calculations,　such　as　convolutions.　We　improve　the　Freivalds　algorithm　to　enhance　the　ability　to　detect　tiny　perturbations　by　strengthening　randomization.　As　the　verification　process　is　also　susceptible　to　FIAs,　DarkneTF　offloads　the　verification　process　into　Trusted　Execution　Environments　(TEEs).　This　scheme　ensures　the　verification　process＇s　security　and　allows　for　accelerated　model　inference　using　the　integrated　GPUs.　Experimental　results　show　that　GPU-accelerated　FT　inference　on　HiKey　960　achieves　notable　speedups　ranging　from　3.46x　to　5.57x　compared　to　FT　inference　on　a　standalone　CPU.　The　extra　memory　overhead　incurred　FT　inference　remains　at　an　exceedingly　low　level,　with　a　range　of　0.46%　to　10.22%.　The　round-off　error　of　the　improved　Freivalds　algorithm　is　below　2.50×10−4,　and　the　accuracy　of　detecting　FIAs　is　above　92.73%.　©　2024　Elsevier　B.V.