Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

We present Random Partition Relaxation (RPR), a method for strong quantization of the parameters of convolutional neural networks to binary (+1/-1) and ternary (+1/0/-1) values.
Starting from a pretrained model, we first quantize the weights and then relax random partitions of them to their continuous values for retraining before quantizing them again and switching to another weight partition for further adaptation.  
We empirically evaluate the performance of RPR with ResNet-18, ResNet-50 and GoogLeNet on the ImageNet classification task for binary and ternary weight networks.
We show accuracies beyond the state-of-the-art for binary- and ternary-weight GoogLeNet and competitive performance for ResNet-18 and ResNet-50 using a SGD-based training method that can easily be integrated into existing frameworks.
Deep neural networks (DNNs) have become the preferred approach for many computer vision, audio analysis and general signal processing tasks.
However, they are also known for their associated high computation workload and large model size.
These are great hurdles to their wide-spread adoption due to the consequential cost, which is often prohibitive for low-power, mobile and alwayson applications.
This concern has driven a lot of research into various DNN topologies and their basic building blocks in order to reduce the required compute cost at a small accuracy penalty.
Furthermore, efforts have been made towards compressing the models from often hundreds of megabytes to a size that is suitable for over-the-air updates and does not negatively impact the user experience by taking up lots of storage on consumer devices and long loading times.
Recent research into specialized hardware accelerators has shown that improvements by 10-100× in energy efficiency over optimized software are achievable (Sze et al., 2017) .
These accelerators can be integrated into a system-on-chip like those used in smartphones and highly integrated devices for the internet-of-things market.
These devices still spend most energy on I/O for streaming data in and out of the hardware unit repeatedly as only a limited number of weights can be stored in working memory-and if the weights fit on chip, local memories and the costly multiplications start dominating the energy cost.
This allows devices such as (Andri et al., 2018) Quantizing neural networks is crucial to allow more weights to be stored in on-chip working memory or to be loaded more efficiently from external memory, thereby reducing the number of repeated memory accesses to load and store partial results.
Complex network compression schemes cannot be applied at this point as decompression is often a lengthy process requiring a lot of energy by itself.
Furthermore, by strongly quantizing the network's parameters, the multiplications in the convolution and linear layers can be simplified, replaced with lightweight bit-shift operations, or even completely eliminated in case of binary and ternary weight networks (BWNs, TWNs) (Zhou et al., 2017) .
State-of-the-art training method for binary and ternary weight networks based on alternating optimization of randomly relaxed weight partitions