Spiking neural networks are closer to biological neural functioning because they classify information based on temporal spikes rather than continuous levels. This also makes them more energy efficient if somewhat more complex than digital neural networks.
CEA-Leti's claims comes in spite of the fact that BrainChip Holdings Ltd. has launched its Akida Neuromorphic System-on-Chip (SoC) back in September 2018, claiming to be the first company with a hardware implementation of a spiking neural network architecture (see BrainChip launches spiking neural network SoC).
CEA-Leti built its chip in a 130nm CMOS manufacturing process with analog neurons and resistive-RAM-based (ReRAM) synapses, integrated monolithically on top of CMOS devices. The ReRAM devices are based on titanium-oxide and hafnium-oxide layers between titanium-nitride electrodes, transition-metal oxide two terminal ReRAMs.
CEA-Leti spiking neural network chip floor plan. Source: IEDM.
The test chip includes 11,500 1T-1R ReRAM cells and demonstrated an accuracy of 84 percent at recognizing handwritten digits from the MNIST database, with 5x lower energy dissipation at the synapse and neuron level (3.6 pJ) versus other chips that use formal programming methods for image classification.
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