BNN implementation using memristors is accurate and efficient than CMOS and FPGA
By using a complete memristor-Based Bayesian Neural Network implementation, Researchers at CEA Leti able to handle a real world case of analyzing recordings of heart' health-condition called arrhythmia to classify its types with precise aleatoric and epistemic uncertainty.
Pic above: Wafer-level characterization of memristor crossbar arrays using a prober, highlighting detailed probe card analysis at an enlarged scale. Bayesian neural networks is said be good option for safety-critical, sensory-processing applications requiring accurate decisions based on a small amount of noisy input data. But Bayesian neural networks require high computation and lot of power when it is done on CMOS-based ASICs and FPGAs, due to huge transfer of data between processor and memory. Implementing the networks in hardware requires a random number generator to store synaptic weights and such probability distributions. It also requires massive parallel multiply-and-accumulate (MAC) operations. Researchers have innovatively used crossbars of memristors that naturally implement the multiplication between the input voltage and the probabilistic synaptic weight through Ohm's law, and the accumulation through Kirchhoff's current law, to significantly lower power consumption. CEA Leti researchers have pub...
Pic above: Wafer-level characterization of memristor crossbar arrays using a prober, highlighting detailed probe card analysis at an enlarged scale. Bayesian neural networks is said be good option for safety-critical, sensory-processing applications requiring accurate decisions based on a small amount of noisy input data. But Bayesian neural networks require high computation and lot of power when it is done on CMOS-based ASICs and FPGAs, due to huge transfer of data between processor and memory. Implementing the networks in hardware requires a random number generator to store synaptic weights and such probability distributions. It also requires massive parallel multiply-and-accumulate (MAC) operations. Researchers have innovatively used crossbars of memristors that naturally implement the multiplication between the input voltage and the probabilistic synaptic weight through Ohm's law, and the accumulation through Kirchhoff's current law, to significantly lower power consumption. CEA Leti researchers have pub...
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