Usha M

Harnessing Hardware‐Accelerated Neural Network Inference: Pioneering the Future of Embedded Intelligence In the evolving landscape of embedded systems, the integration of dedicated neural network (NN) accelerators represents a paradigm shift that promises

Federated Learning on Edge Devices: Revolutionizing AI with Privacy and Efficiency In the rapidly evolving landscape of embedded systems and artificial intelligence, federated learning stands out as a transformative approach that offers a

Embracing Continual Learning at the Edge: The Future of Intelligent Embedded Systems In the rapidly evolving landscape of embedded systems, the integration of continual learning—or online learning—directly at the edge is

Unlocking New Horizons in Embedded Systems with Reinforcement Learning Reinforcement learning (RL) is rapidly transforming the landscape of embedded control, enabling devices to adapt in real-time to changing environments with unprecedented autonomy. This

As embedded systems continue their rapid evolution, the integration of AI is redefining what these compact devices can achieve. Among the emerging innovations, TinyGAN—an ultra-lean variant of generative adversarial networks—stands out