This article makes a lot of apples to oranges comparisons that are confusing: 1. The article makes the claim this is the first SNN processor, but then states t

This article makes a lot of apples to oranges comparisons that are confusing: 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.