Reconfigurable Deep Neural Network Techniques for Supervised Learning(1/3)

Deep neural network (DNN) is one widely used artificial intelligence (AI) technique. The DNN engine in edge devices for the AI applications should meet the requirements of low energy, high flexibility, and short end-to-end latency. Therefore, we attempt to develop a reconfigurable DNN (RDNN) engine for the applications of human posture recognition and robotics to meet the requirements under the grand project entitled “Reconfigurable Deep Neural Network Engine for human posture recognition and robotics”. The innovations of the developed RDNN include: 1) support the supervised and reinforcement learning algorithms; 2) support various DNN models including compressed models; 3) use hybrid digital and analog computing units to minimize energy consumption; and 4) using posture and behavior recognition system to verify the performance of RDNN engine.This subproject entitled “Reconfigurable Deep Neural Network Techniques for Supervised Learning” attempts to develop reconfigurable design techniques for DNN with supervised learning. The innovations of this subproject include: 1) reconfigurable technique for CNNs under the constraint of end-to-end latency; 2) reconfigurable technique for various CNN models; 3) reconfigurable technique for hybrid digital and analog computing units.