1 code implementation • 5 Jan 2023 • Surat Teerapittayanon, Marcus Comiter, Brad McDanel, H. T. Kung
We then show that these fragments can be stitched together to create neural networks with accuracy comparable to that of traditionally trained networks at a fraction of computing resource and data requirements.
1 code implementation • 19 Jul 2019 • Surat Teerapittayanon, H. T. Kung
A main feature of DaiMoN is that it allows peers to verify the accuracy improvement of submitted models without knowing the test labels.
no code implementations • 17 Jun 2019 • Marcus Comiter, Surat Teerapittayanon, H. T. Kung
CheckNet is like a checksum for neural network inference: it verifies the integrity of the inference computation performed by untrusted devices to 1) ensure the inference has actually been performed, and 2) ensure the inference has not been manipulated by an attacker.
no code implementations • 21 Oct 2017 • Bradley McDanel, Surat Teerapittayanon, H. T. Kung
At inference time, the number of channels used can be dynamically adjusted to trade off accuracy for lowered power consumption and reduced latency by selecting only a beginning subset of channels.
3 code implementations • 6 Sep 2017 • Surat Teerapittayanon, Bradley McDanel, H. T. Kung
Deep neural networks are state of the art methods for many learning tasks due to their ability to extract increasingly better features at each network layer.
2 code implementations • 6 Sep 2017 • Bradley McDanel, Surat Teerapittayanon, H. T. Kung
Beyond minimizing the memory required to store weights, as in a BNN, we show that it is essential to minimize the memory used for temporaries which hold intermediate results between layers in feedforward inference.
1 code implementation • 6 Sep 2017 • Surat Teerapittayanon, Bradley McDanel, H. T. Kung
In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.