Neuromorphic Defenses through Advanced Shader Techniques in GPU-Accelerated AI Security

As the complexity and sophistication of GPU-accelerated AI systems continue to rise, their security against various side-channel attacks has become a major concern. This paper introduces a novel framework for implementing neuromorphically inspired defences through advanced shader techniques in GPU-accelerated AI security. By capitalising on the specific features of neuromorphic computing and the power provided by modern GPU shader programs, it presents a promising defence strategy that can alleviate timing attacks, power analysis, and cache attacks more robustly and efficiently. The paper introduces a secure shader execution model based on neuro-mimicked functionalities like spiking neural networks and memristive synapses to hide secretive information as well as enhance the resilience of AI workloads against side-channel threats. The proposed solution is highly compatible with existing conventional General Processing Graphics Processing Units (GPGPU) frameworks with low overhead upscaling potentials that empower its deployment toward securing real-world General Processing Graphics Processing Units (GPGPU) accelerated machine learning systems, opening new horizons of protecting potentially vulnerable machine learning applications used in voluminous sensitive domains such as health care, finance or self-driving.