What is Inference Gap & how to overcome it in Deep Learning Models
By the minute, deep learning models trespass every industry, making neural networks more and more extensive. How do you ensure accuracy in such a situation? That’s easy- just increase the number of training parameters. And then what? As a result, the models tend to become sluggish in their performance due to the increased size of the additional parameters. Can it get any worse? Hate to break it to you, but it can. Enter, inference gap!
Key Aspects of Inference
Inference gap occurs due to the feeding of excessive parameters into a model that lacks the computational power to deliver the outcome promptly. As a result, the performance deteriorates due to the inference computational barriers. This also results in the model used during inference being trained on old data causing poorer than expected results at the time of inference and there you have it- an inference gap.
Ideally, deep learning inference is associated with using a trained deep learning model to make predictions on unseen data. The process consists of a forward pass in the form of training for prediction. Unlike a training process for deep learning models, inference does not involve a backward pass for the computation errors and weight updates. So how does inference gap occur?
Deep learning models are typically preferred for image classification, natural language processing, and several AI-related tasks. However, these tasks are complex and consist of a large volume of data. The processing of such data volumes requires multiple layers of neurons and millions of connected weights for performing a task efficiently and accurately. As the deep learning models tend to be large to handle complex tasks, the computing capacity, memory requirements, and energy consumed to perform the tasks increase significantly. Also, with multiple large datasets, the training process becomes slower. Besides, the slow training process makes a model move into the production phase with stale data, thereby resulting in poor performance. Therefore, a deep learning model is often optimized to meet the computation and performance requirements to work on real-world data.
Three-Axis Inference Optimization by NVidia
Another aspect of inference with deep learning models is the extended latency, i.e. the response time from the time of data feeding until the model’s outcome is achieved. A pioneer in the field of deep learning: Andrew NG, commented that Baidu’s deep learning technology for speech recognition requires four terabytes of data and several billions of mathematical operators for computing across its training cycle. Therefore, an inference gap arises in such types of production environment scenarios, especially for profit-based organizations. Such deep learning models are slower in their production phase, causing a demand and supply mismatch in the market. This is true for even problems such as real-time predictions on organizational data as well.
Current Challenges of Inference Gap in Deep Learning Models
- Due to high throughput, the models face difficulties processing high volumes of data and high-velocity data. When the inference increases, the cost per inference increases.
- A key challenge while addressing the inference gap is the low response time. The deliverables of the applications are not feasible over real-time scenarios. The applications that require faster response time, such as real time customer interactions, object detection, autonomous vehicles, smart driving assistance, struggle in performance, negatively impacting the user experience. With a massive amount of power and memory usage, there is an increased running cost and inefficient deployment of the application.
- While the dependencies increase, productivity suffers from increased time, and the production struggles with the demand.
Addressing the Inference Gap
Deep learning applications requiring faster latency require an inference of milliseconds to address the outcome of such tasks. For example, deep learning-powered autonomous vehicles require a faster response time to have the appropriate outcome to avoid any mishap. There are several ways to optimize deep neural networks to address the inference gap possibilities while reducing the energy cost and latency. Some of these are:
Pruning involves the identification of the group of artificial neurons that are rarely used for specific tasks. These neurons are removed from the model without disrupting the accuracy model. This enables a reduction in the size of the model and improves the latency time significantly. While the weights of the networks of simpler models such as AlexNet can be easily compressed using pruning, the techniques with the likes of end-to-end pruning reduce the need for fine-tuning of the neural network. In contrast, filter pruning tends to reduce the accuracy of the existing architectures.
Another approach is quantization, which involves altering the numerical precisions for a reduced model size and improved latency of the model. Some developers opt for a multiple-layer fusion of the neural network to be used in a single computational process. The primary reason for quantization is to produce smaller networks that are capable of achieving computational efficiency. However, they are affected by problems such as information loss.
3. Smart Pipelines
Smart data pipelines that can feed data for training to models in real-time and then serve those trained models for inference can ensure that no inference gap exists. If these pipelines can chunk and feed data for training in quick batches it is an added bonus. All this allows for an inference with better results and one that closes the gap. There are specialized solutions in the market these days like Fluid AI’s analytics solution that helps make this a very efficient process.
4. Neural Architecture Search (NAS)
In recent times, the popularity of NAS increases as it automates the selection of efficient architectures capable of performing at a lesser runtime while preserving the model’s accuracy. With the help of a search strategy, all the possible architectures along with the subsets are evaluated. NASNet, AmoebaNet are some examples. However, a massive amount of computational power is required to find such architectures. Several hours of GPU are needed to find the best possible architecture.
5. Software Accelerators
Software accelerators are used to improve the computational performance without changing the overall model. It is similar to a software program that translates the deep learning model’s performance in real-time operations on the hardware device.
The hardware devices are located at the bottom of the acceleration stack. The hardware comprises the CPUs and GPUs and specialized accelerators such as Google TPU. A more robust computational mechanism ensures faster deep learning inference. For example, GPUs can increase throughput as opposed to a CPU device.
Additional factors that may improve inference include easier-to computes network, fine-tuning, use of low-precision arithmetic, knowledge transfer from a complex model to a smaller model, an efficient model that involves sparsely connected neurons in its architecture, re-usability of computation for problems such as time-series data etc.
We get it, it seems like the inference gap has become a double edged-sword with no solution worth its while. The good news? Companies like Fluid AI have recognized the inference gap and have successfully addressed the associated problems with smart data pipelines that allow faster processing and run some of the above steps automatically as a part of their pipeline. Besides, the pipelines allow more comfortable handling of data with quick and daily refreshing to ensure that no stalled data is involved in the production environment.
It is critical for organizations to opt for the appropriate solutions instead of following the market trend, as achieving accelerated inference without sacrificing accuracy is challenging but achievable.
Don’t get inferenced out, #MakeITHappen!