BTSAM: Balanced Thermal-State-Aware Mapping Algorithms and Architecture for 3D-NoC-Based Neuromorphic Systems

Neuromorphic computing systems are biologically inspired approaches created from many highly connected neurons to model neuroscience theories and solve machine learning problems. They promise to drastically improve the efficiency of critical computational tasks such as decision-making and perception. Combining neuromorphic computing systems and 3D interconnect (3D-NoC) technology leads to an advanced architecture that inherits the benefits of both computing and interconnect paradigms. However, designing large-scale neuromorphic systems based on 3D-NoC faces several challenges, including thermal power, power distribution, increased power density at the heat sink interface, and fabrication requirements. This work tackles the thermal issues in designing large-scale neuromorphic systems by proposing a Balanced Thermal-State-Aware Mapping (BTSAM) for 3D-NoC-based neuromorphic systems. This includes a Periodic Activity Scoring (PAS), a Seesaw Neuron Clustering (SNC) method, and a thermal-aware genetic algorithm to eliminate hotspots, balance the thermal state, and lower the temperature while keeping the system’s accuracy acceptable. Evaluation results on various system configurations demonstrate a notable up to 12.4 K and 5.2 K temperature reduction compared to linear methods and HeterGenMap, respectively, and a
$4\times $
increase in Mean-Time-to-Failure (MTTF), with an acceptable power and area overheads and little degradation of the communication cost.