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:

Despite the state-of-the-art accuracy of Deep Neural Networks (DNN) in various classification problems, their deployment onto resource constrained edge computing devices remains challenging due to their large size and complexity.
Several recent studies have reported remarkable results in reducing this complexity through quantization of DNN models.
However, these studies usually do not consider the changes in the loss function when performing quantization, nor do they take the different importances of DNN model parameters to the accuracy into account.
We address these issues in this paper by proposing a new method, called adaptive quantization, which simplifies a trained DNN model by finding a unique, optimal precision for each network parameter such that the increase in loss is minimized.
The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly.
Since this problem uses linear functions, it is computationally cheap and, as we will show, has a closed-form approximate solution.
Experiments on MNIST, CIFAR, and SVHN datasets showed that the proposed method can achieve near or better than state-of-the-art reduction in model size with similar error rates.
Furthermore, it can achieve compressions close to floating-point model compression methods without loss of accuracy.
Deep Neural Networks (DNNs) have achieved incredible accuracies in applications ranging from computer vision BID16 to speech recognition BID7 and natural language processing BID5 .
One of the key enablers of the unprecedented success of DNNs is the availability of very large model sizes.
While the increase in model size improves the classification accuracy, it inevitably increases the computational complexity and memory requirement needed to train and store the network.
This poses challenges in deploying these large models in resource-constrained edge computing environments, such as mobile devices.
These challenges motivate neural network compression, which exploits the redundancy of neural networks to achieve drastic reductions in model sizes.
The state-of-the-art neural network compression techniques include weight quantization BID4 , weight pruning BID6 , weight sharing BID6 , and low rank approximation BID18 .
For instance, weight quantization has previously shown good accuracy with fixed-point 16-bit and 8-bit precisions BID17 BID14 .
Recent works attempt to push that even further towards reduced precision and have trained models with 4-bit, 2-bit, and 1-bit parameters using quantized training methods BID9 BID4 .Although
these quantization methods can significantly reduce model complexity, they generally have two key constraints. First, they
ignore the accuracy degradation resulting from quantization, during the quantization, and tend to remedy it, separately, through quantized learning schemes. However, such
schemes have the disadvantage of converging very slowly compared to full-precision learning methods. Second, they
treat all network parameters similarly and assign them the same quantization width 1 . This is while
previous works BID9 BID6 have shown different parameters do not contribute to the model accuracy equally. Disregarding
this variation limits the maximum achievable compression.In this paper, we address the aforementioned issues by proposing adaptive quantization. To take the
different importances of network parameters into account, this method quantizes each network parameter of a trained network by a unique quantization width. This way, parameters
that impact the accuracy the most can be represented using higher precisions (larger quantization widths), while low-impact parameters are represented with fewer bits or are pruned. Consequently, our method
can reduce the model size significantly while maintaining a certain accuracy. The proposed method monitors
the accuracy by incorporating the loss function into an optimization problem to minimize the models. The output of the optimization
problem is an error margin associated to each parameter. This margin is computed based
on the loss function gradient of the parameter and is used to determine its precision. We will show that the proposed
optimization problem has a closed-form approximate solution, which can be iteratively applied to the same network to minimize its size. We test the proposed method using
three classification benchmarks comprising MNIST, CIFAR-10, and SVHN. We show that, across all these benchmarks
, we can achieve near or better compressions compared to state-of-the-art quantization techniques. Furthermore, we can achieve compressions
similar to the state-of-the-art pruning and weight-sharing techniques which inherently require more computational resources for inference.
We evaluate adaptive quantization on three popular image classification benchmarks.
For each, we first train a neural network in the floating-point domain, and then apply a pass of algorithm 3 to compress the trained model.
In both these steps, we use the same batchsize to calculate the gradients and update the parameters.
To further reduce the model size, we tune the accuracy of the quantized model in the floating-point domain and quantize the tuned model by reapplying a pass of algorithm 3.
For each benchmark, we repeat this process three times, and experimentally show that this produces the smallest model.
In the end we evaluate the accuracy and the size of the quantized models.
Specifically, we determine the overall number of bits (quantization bits and the sign bits), and evaluate how much reduction in the model size has been achieved.We note that it is also important to evaluate the potential overheads of bookkeeping for the quantization widths.
However, we should keep in mind that bookkeeping has an intricate relationship with the target hardware, which may lead to radically different results on different hardware platforms.
For example, our experiments show that on specialized hardware, such as the one designed by Albericio et al. (2017) for processing variable bit-width CNN, we can fully offset all bookkeeping overheads of storing quantization depths, while CPU/GPU may require up to 60% additional storage.
We will study this complex relationship separately, in our future work, and in the context of hardware implementation.
In this paper, we limit the scope to algorithm analysis, independent of the underlying hardware architecture.
In this work, we quantize neural network models such that only parameters critical to the accuracy are represented with high precision.
The goal is to minimize data movement and simplify computations needed for inference in order to accelerate implementations on resource constrained hardware.
To achieve acceleration, the proposed technique prunes unnecessary parameters or reduces their precisions.
Combined with existing fixed-point computation techniques such as SWAR BID2 or Bit-pragmatic computation (Albericio et al., 2017) , we expect these small fixed-point models to achieve fast inference with high accuracies.
We have confirmed the effectiveness of this technique through experiments on several benchmarks.
Through this technique, our experiments show, DNN model sizes can be reduced significantly without loss of accuracy.
The resulting models are significantly smaller than state-of-the-art quantization technique.
Furthermore, the proposed Adaptive Quantization can provide similar results to floating-point model compression techniques.
An adaptive method for fixed-point quantization of neural networks based on theoretical analysis rather than heuristics.