Moving AI to the Real World: 9 Common Challenges for Deploying Machine Learning
The deployment of machine learning solutions can be fraught with challenges. First, not all applications have the same needs. Some must prioritize speed or ease-of-use, while others require low latency or minimal total cost-of-ownership (TCO). Furthermore, GPUs — the chips often used for deep learning — are the origin of many deployment challenges due to their cooling needs, short life span, inability to adapt to different environments, and steep maintenance costs. One common desire is to deploy machine learning without effort and closer to the user.
FPGAs are a much better fit for machine learning inference than GPUs, but may be intimidating due to the advanced expertise required to program them. Mipsology’s Zebra eliminates this complexity, empowering developers to leverage this more adaptable chip and overcome nine of the most common AI challenges:
1. Environmental Adaptability
GPUs work fine in the climate-controlled datacenter, but are much less reliable “in the real world.” To be fair, they were originally designed for gamers, so environmental factors were less critical, let alone the necessity of running 24/7 for years. Nevertheless, they are far less effective in the field than Zebra-powered FPGAs, which enable developers to implement AI in a wide variety of use cases.
Although GPUs are more commonly used than other chips, they have a short lifespan and reportedly can break randomly without warning. This isn’t a big deal for gaming applications, since new consoles are introduced every few years. But when a developer designs a system that is meant to last 10-15 years, GPUs are a terrible match. They only last 2-4 years before failing, which creates severe maintenance challenges. Over the lifespan of a system, the original GPU costs can multiply 3-4X.
Many developers need a long-lasting solution that can lower TCO by working for years on some of the most complex applications, both in the data center and the field. This is where Zebra-powered FPGAs excel.
3. Power and Cooling
Large GPUs consume significant power, creating an energy sink spot when packed into a data center. A GPU may boost the power consumption of a cluster node by up to 30%, which obviously increases data center costs. The cooling systems required to avoid overheating add to the cost. Zebra-powered FPGAs require less power and cooling than GPUs, so they have a much smaller impact on the TCO of the hardware platform.
Only a few types of GPUs are available, and their prices differ dramatically. In contrast, FPGAs come in a variety of sizes and price points, enabling developers to pick exactly what they need for their application. Zebra supports 14 cards from various providers, with more on the way.
Additionally, when computing requirements increase, going to a larger GPU can be pricey. Switching from one FPGA to another is much less expensive.
There are many reasons why neural networks need to be modified or retrained for field deployment. Perhaps computation is taking too long, data center costs were not considered initially, the project goals were changed, or there is a new neural network that makes a big difference in quality. Regardless, you will need to bring your AI team back in to retrain it and deal with the limited capability of the deployed hardware, which may not be able to meet market needs.
Zebra-powered FPGAs, on the other hand, are “field-programmable.” This means they can be tuned for any function or application, and then reprogrammed at any time, even if you don’t know the needs yet. This saves considerable time and money spreading hardware cost over many years.
99% of neural networks are trained on GPUs. However, FPGAs are far better suited for inference. The problem is that most developers do not have the specialized expertise required to program FPGAs.
Zebra removes this complexity entirely. Developers do not need to know anything about FPGAs to run a NN model on a Zebra-powered FPGA. Zebra is delivered with pre-compiled FPGA binary files, so users don’t need to program the FPGA; they only see their usual framework, such as TensorFlow or PyTorch.
Zebra-powered FPGAs execute tens of thousands of operations, 650 million times each second, with more efficient throughput and lower latency than GPUs, which are less parallel. Additionally, FPGA microcode can be updated without changing silicon. Mipsology releases software updates to improve the performance multiple times a year.
Many AI application developers want to implement additional features on their chips. This is possible with multiple-component semiconductors, but it requires additional testing, increases costs and reduces reliability. In contrast, Zebra-powered FPGAs enable developers to include other features besides AI in the chip to accelerate the application further.
9. Quality of results
Some deployments use specialized ASICs, even if doing so causes drastic changes to the NN. ASICs currently in use were most likely designed before the latest neural networks, making them less than ideal. The common idea that ASICs are better would be true only if deep learning had stopped progressing years ago, which is not the case. Their proprietary tools are also a challenge, as they force retraining in many cases with unknown impacts to quality. It is often only late in the project that ASICs’ limitations are seen, putting the full project at risk. Zebra-powered FPGAs, on the other hand, keep up with the latest innovations in machine learning and transparently use GPU-trained neural networks to deploy them efficiently.
Zebra-powered FPGAs can tackle all nine of these common AI deployment challenges, enabling developers to design a broad range of AI applications that perform well in any location — from datacenters and the desktop to the cloud, the edge, and in embedded applications — while lowering TCO considerably.
Contact our team and learn more about Zebra’s capabilities via firstname.lastname@example.org.