Why not compare apples to apples? Take a CNN architecture that gets >95% accuracy, shrink it down until it only gets 82% accuracy, then run it on commercially available non-spiking NN hardware like a Movidius Myriad or Apple's Neural Engine and measure the speed and power consumption.
Also... LeNet isn't THAT bad on CIFAR-10. It can easily reach 75% after 200 epochs.
I think these specialized chips can be useful in mobile or low power applications, but for servers the flexibility of a GPU or reprogrammable FPGA is worthwhile. Advancements in ML are frequent and nobody wants to be stuck with hardware that can't run a newer architecture.
You are being generous. "Zero" does not qualify for "few".
On a more serious note:
> I don't get the appeal of neuromorphic spiking chips even in academia. We don't have a definite , final spiking neuron model, and these architectures are too constrained to use them for finding it.
I think at the moment there are some people that simply want to grab a bunch of the IP rights and position themselves in the hardware market. I think they know that their networks are not up to scratch yet, but they also believe that somebody will figure out how to implement backprop in a spiking network soon, and then they are in a good position to build something worthwhile. On the theory side, there are some papers coming pretty darn close that have come out in the last year.
1. The article makes the claim this is the first SNN processor, but then states they will first make an FPGA. They already reference existing processors, that have real debugged and fully functional ASICs, which already exist and are clearly the first SNNs. This is not the first SNN based FPGA design, nor the first functional SNN ASIC. For context, the existing SNN ASIC TrueNorth taped out in ~2012, and NeuroGrid in ~2010.
2. They compare Cifar-10 results to ImageNet on the same chart. This is not apples to apples, as an architecture can get bus bound with larger image patches, weights and activations, etc. Once it becomes bus bound these architectures can lose efficiency.
3. They talk about low power (<5W) architectures being compelling, but this does not include the TX2 (10-20W with the GPU going), and TrueNorth and NeuroGrid are at least an order of magnitude smaller (i.e. <<500mW). They omitted mobile chips and Qualcomm's Hexagon which is extremely compelling at the ~1-2W range.
4. One of the neat things about the TrueNorth and NeuroGrid architecture is that they are asynchronous; when little activity is happening the chip can draw less power. Even the TX2 has some property of this by dynamically scaling power for the GPU, and perhaps I missed this but it does not seem supported by this architecture. Idle power draw can be important!
Once you remove a few of those datapoints and compare apple to apple this architecture seems less compelling. For what reads like a sales pitch, they could do a better job being straightforward about why this makes sense. And if it doesnt make sense, it will be yet another failed "AI" chip.
But then you add layers and transfer functions, so it's more like:
And then you solve for each mn and bn, using f^(-1), which is why smooth transfer functions are preferred, and then on some networks you can visualize the training space, with the derivative pointing toward the most optimal position.
But, a spiking neural network that isn't smooth seems like you wouldn't form clean gradients for training, so that's actually a really good question... Seems like it wouldn't work correctly, or would be incredibly difficult to train. Of course maybe "spiking" is a name for another part of the behavior, and not the transfer function itself.
They don't exceed ANNs at this point, but are closer to biological neurons. Some researchers are looking into how the backpropagation of errors might be implemented in spiking networks. Example: https://www.cell.com/neuron/abstract/S0896-6273(13)01127-6?c...