Neuromorphic: brainlike computing, page-13

Just quickly look in scholar, notice the difference however:

Weebit research published ~April this year, still working on MNIST....
vs
Brainchip has a development environment that lets you install and start building products with their library in under 2 minutes...



Weebit Nano

Fully-Integrated Spiking Neural Network Using SiOx-Based RRAM as Synaptic Device
https://ieeexplore.ieee.org/abstract/document/9073840
Date Added to IEEE Xplore: 23 April 2020
Abstract:
This paper presents, to the best of the authors' knowledge, the first complete integration of a Spiking Neural Network combining analog neurons and SiO x -based resistive memory (RRAM) synapses. The implemented topology is a perceptron, and the circuit is aimed at performing MNIST digits classification. An existing framework was adapted for off-line learning and weight quantization, and the network was later converted into its spiking equivalent. The test chip, fabricated in 130 nm CMOS, shows a classification accuracy of 82%, with a 180 pJ energy dissipation per spike

Brainchip

This tutorial illustrates how to use a pre-trained Akida model to process the MNIST dataset.
https://doc.brainchipinc.com/examples/plot_gxnor_mnist.html
Depending on the number of samples you run, you should find a performance of around 99% (99.35% if you run all 10000 samples).

That should give you a very clear comparison between the two