XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks · HackerPapers discussions

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by buildbot

Sat, 09 Nov 2024 18:28:30 GMT

Exactly as xnornet was doing way back in 2016 - shadow 32bit weights, quantized to 1 bit during the forward pass. https://arxiv.org/abs/1603.05279 I personally have a pretty negative opinion of the bitnet paper.

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by Qworg

Tue, 14 Aug 2018 05:34:31 GMT

There are low-bit networks as well: https://arxiv.org/abs/1603.05279 That technology is now a spinoff of AI2 and UW: https://www.xnor.ai/

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by emcq

Sat, 11 Feb 2017 21:27:32 GMT

The reality is that TrueNorth can use CNNs and be trained with backprop to achieve good performance with networks like ImageNet despite LeCun's comments [0, 1] LeCun's primary critique is that binary wont work; "to get good results on a task like ImageNet you need about 8 bit of precision on the neuron states". There was no evidence for his claim then, and now this is clearly false [2, 3]. LeCun's post is based more in pride than reason; he spends most of the time talking about NeuFlow which at one point was a competitor to True North for funding. In the end, NeuFlow never became a chip, but…

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by paulsutter

Sat, 11 Feb 2017 19:00:42 GMT

The article misses the core issue: neural network architectures are still in flux. "Neuromorphic" chips are hardwired to one architecture, which makes them power efficient but less flexible. When designs are more stable, such chips could be more practical. Meanwhile, upcoming (non-neuromorphic) AI processors are taking two directions: larger numbers of simplified GPU-type cores (such as NVIDIA Xavier and Intel's Lake Crest/Nervana chips), and FPGAs. Simplifying cores means lower precision, as fp32 and fp64 are overkill for neural networks and take up lots of silicon. The current NVIDIA Pascal…

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by emcq

Mon, 23 Jan 2017 04:53:05 GMT

XNOR-Net is not currently part of TensorFlow, although there is a feature request to support it. [0] The main difference is that XNOR-Net uses 1 bit quantized weights and XNOR+popcount to approximate the dot product of convolution, which can be implemented very efficiently by using 64-bit arithmetic instructions on a CPU to operate over 64 1-bit components in parallel. [1] TensorFlow's optimization is also using discretized weights, but AFAIK targeted at 8-bit quantized weights. [2] [0] https://github.com/tensorflow/tensorflow/issues/1592 [1] https://arxiv.org/pdf/1603.05279v4.pdf [2]…

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by dharma1

Thu, 12 Jan 2017 07:46:23 GMT

Nice, looks like about 10x speed up for this classification task. I think there are big gains to be made in lower precision inference too. Lots of people doing interesting work in that area, check out these guys - https://xnor.ai/ https://arxiv.org/abs/1603.05279

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by sapphireblue

Fri, 15 Jul 2016 13:07:19 GMT

This is incorrect. With custom ASIC that implements ultra-wide low precision SIMD arithmetic (f16, i16, i8) it is possible to squeeze an order of magnitude or more speedup over conventional GPGPUs using same area and power. GPGPUs have to have big f32 (and even f64!) FPUs, and then some additional 3d-specific hardware overhead, that's why they are still suboptimal for deep machine learning. Google's TPU and Nervana's similar ASIC illustrate this point. Also you don't need the management cores to be fast, it suffices to have lots of GHz+ cores controlling ultra-wide SIMD (as Intel's Xeon Phi…

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — mentioned by ericflo

Mon, 18 Apr 2016 19:39:24 GMT

Since it seems this paper's primary focus is on performance, it'd be interesting to see how this technique stacks up against one of those fancy new binary networks (e.g. http://arxiv.org/abs/1603.05279)

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks — HN submission

Sat, 19 Mar 2016 23:02:32 GMT

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

74 points; 17 discussion replies on HN. Paper and source history. A submission is not an endorsement.