Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 (2016 · 2,143 citations)
This work tackles the cost of running deep networks on low-power hardware. Modern DNNs depend on energy-hungry GPUs and full-precision arithmetic. The authors propose Binarized Neural Networks (BNNs), in which weights and hidden activations take only the values +1 or −1. These binary values are used both at inference…
BinaryConnect: Training Deep Neural Networks with binary weights during propagations (2015 · 1,885 citations)
Multipliers are the most area- and energy-hungry parts of digital neural network hardware. This paper asks whether a deep network can be trained while its weights take only two values (+1 or −1) during computation, so that most multiply-accumulate operations become additions and subtractions. BinaryConnect binarizes…
Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations (2016 · 1,610 citations)
The paper tackles the high cost of running deep networks, whose many multiply-accumulate operations make them slow and power-hungry on ordinary hardware and low-power devices.

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Updated 2 November 2026