Ant Colony Optimized Convolution Neural Network-Long Short Term Memory Based Energy Conservation in Variation-Tolerant Near-Threshold Processor

When considering the reduction of profit margins and improvement of energy efficiency in processors operating at near-threshold and sub-threshold levels, exploring timing error resilience might be seen as a potentially profitable option.Near-threshold (NT) circuit design is increasingly used for building energy-efficient digital circuits.One drawback was a drop in performance due to a reduction in driving current.To maximize energy economy and minimize performance deterioration, some studies advocate nontraditional (NT) chip multiprocessors.The creation of energy-efficient artificial intelligence technologies has gained popularity recently.Graphics processing units and generalpurpose CPUs could not match the goal of efficiency and performance.A semiconductor with many processing units was created to do this.This goal was achieved by engineering the hardware with unique features.Therefore, this study constructs an ACO_CNN+LSTM (Ant Colony Optimised Convolution Neural Network-Long Short Term Memory) neural network to achieve lower power consumption without compromising performance.In this method, Ant Colony Optimization (ACO) is used to explore and select optimal configurations of CNN and LSTM architectures, minimizing energy usage through efficient layer selection, filter size adjustment, and neuron pruning.By incorporating Dynamic Voltage and Frequency Scaling (DVFS) and operating at near-threshold voltages (NTV) -where the supply voltage is just above the transistor threshold -power consumption is further reduced, as the processor can function at lower voltage levels while tolerating variations in performance.This hybrid ACO-driven optimization enhances both CNN feature extraction and LSTM's temporal processing by selecting the most energy-efficient network parameters.As a result, the proposed ACO_CNN+LSTM achieves 12.73% of energy consumption, 9.46 sec of latency, 98.45% of normalized frequency, 22.64% of error rate and 31.67% of processor utilization.