I’ve greatly enjoyed my time as a graduate research intern at DeepScale and so I was pleased when Forrest Iandola asked me to give a talk to his team on deep learning hardware. To keep the talk from being a boring list of slides recounting famous (but dry) neural network hardware papers, we decided to structure it like one of David Patterson’s talks which describe what not to do. The talk was well received, so I decided to reformat it into this blog post. This post does assume some knowledge of DNN processing, but for those of you who are new to the topic I also provide explanations for each piece of advice in spoiler tags. If you want to learn more about DNN processing, I recommend this excellent survey paper by Vivienne Sze and others.
Fun fact by the way: Forrest and I first met way back in 2007 when our respective projects made it to the AAAS national high school science fair! The project I made for that competition developed into my first patent and eventually my first company.
Before we begin, a few disclaimers. 1) While I have read and written many papers on DNN hardware design, I have never built DNN hardware in a corporate context. 2) These are my personal opinions and don’t reflect those held by DeepScale.
Alright, lets make some awful hardware!
Guidelines for Making Bad DNN Hardware
Make One Chip To Rule Them All
Training and inference both need fast access to memory and high parallelism, so they’re pretty much the same thing. This means you can make just one chip while claiming that your system is highly optimized for all use cases.
Why is this so bad?Users interested in training and users interested in inference often have fundamentally different hardware needs. For example: hardware for training is generally evaluated on its throughput above all else, while hardware for inference is typically optimized for latency. Data locality can be different as well since activations must be stored for all layers when training but activations can be discarded during inference. Finally, training hardware will often need to support much higher precision than inference hardware due to the need for gradient computation in training. So while some hardware optimizations improve performance overall, many design decisions will end up placing your hardware somewhere along a trade-off curve. Without a clear focus for what your potential users need, you’ll find that what you’ve built isn’t useful to anyone in particular.
All DNNs are CNNs
The most famous DNN paper in recent memory is AlexNet. From this we can conclude that absolutely all neural networks are convolutional image classifiers. Optimize your system for
3x3(or better yet,
5x5!) convolutions and then claim that your system is a generic neural network accelerator.
Why is this so bad?CNNs are far from the only kind of neural network. Despite their popularity in the hardware research community, Google found that CNNs accounted for only 5% of their total DNN workload. The other big players include Multi-Layer Perceptrons and Recurrent Neural Networks such as Long Short-Term Memory networks. If you plan on making a generic deep learning processor then optimizing for only CNNs is a huge oversight.
It is important to point out however that not all hardware must aspire to be purely generic. After all, part of the reason why the hardware community has been so interested in CNNs is the many interesting and effective ways to accelerate them. This includes my own work! Also, while Google servers may not run CNN inference frequently, vision-heavy applications like self driving cars and augmented reality rely heavily on CNNs. So, rather than claiming that optimizing for CNNs is always a bad idea, I simply want to point out that choosing to specialize for CNN computation must be done intentionally.
Hammer that DRAM
GPUs are good at deep learning and have tiny caches, so learn from this by saving precious area for logic instead of on-chip memory.
Why is this so bad?Good computer architects must always be thinking about the spatial and temporal locality of data in their applications of choice. While many GPU applications don’t have significant temporal locality (hence the reason for small on-chip memory) this is certainly not the case for deep learning workloads. As Song Han and others point out in their paper on the Efficient Inference Engine: even without changing the underlying computation, huge amounts of energy and time can be saved by simply keeping network activations and model parameters on chip. The ShuffleNet V2 paper also has some excellent discussion about how memory hierarchy and memory access patterns impact network performance.
All DNN Computation is Regular
Literally all DNN computation is dense matrix multiplication. The regularity and predictability of this computation means that we can always assume maximum DRAM bandwidth and there’s no need for any hardware or software support for load balancing.
Why is this so bad?As it turns out, lots of DNN computation can be irregular and load balancing can be a hard problem even with fully regular computation. Irregular DNN computation be a result of leveraging sparsity, model compression, or a variety of other techniques. Load balancing issues can come from this irregular computation, but can also come from general scheduling challenges when using systolic arrays like those in the TPU or Eyeriss.
NoM NoM NoM
Clearly the fact that the NoM (Network of the Moment) is so popular now means that it will always be popular. Ensure that your hardware gets the most users by fully optimizing your chip for the NoM.
Why is this so bad?One of the most fundamental concepts in computer architecture is the specialization-generalization trade-off. The fully specialized side of this trade-off would be to take a single algorithm and bake it directly into hardware. This dramatically improves computational efficiency, but optimizing hardware for a single algorithm means that the resulting hardware can’t do anything other than compute that algorithm. So, optimizing your hardware for the network of the moment is a dangerous proposition considering the deluge of papers every year from NIPS, ICML, ICCV, and CVPR. As an example consider the evolution from AlexNet, to ResNet, to SqueezeNet, to MobileNet. The software development cycle (from idea to implementation) can be around 6 months, while the hardware development cycle is often closer to 2 years. This means that overspecialization can lead to your chip being obsolete before it’s even built.
The opposite issue can also come up if there is too much of a focus on antiquated models. Computer architecture researchers in academia are especially susceptible to this issue, but hardware companies can be too if they aren’t careful. For more discussion on this take a look here.
8 Bit Means 8 Bit
DNNs are famously well suited for low precision computation, so we only need to support 8 bit accumulations. Rather than telling anyone that this is what your hardware does, hide it inside your proprietary DNN framework.
Why is this so bad?Computing with 8 bit weights and activations for inference may be one thing, but accumulating in 8 bits will lead to extreme overflow issues. To begin with, multiplying two 8 bit numbers together results in a 16 bit product. Additionally (pun totally intended), many products will be added together as a part of the multiply accumulate chains inherent to matrix-matrix multiplication. For this reason, hardware systems must accumulate with precision significantly larger than that used to store activations and weights. For a more in-depth discussion of the different kinds of precision and their importance in deep neural networks I recommend Chris De Sa’s paper on low-precision SGD, which includes details focused on training which is an entirely different issue altogether.
Latency = 1 / Throughput
Latency is clearly the inverse of throughput, so only report frames per second with large batch sizes.
Why is this so bad?Large batch sizes are often used to increase the temporal locality of data when training. This significantly reduces the average time needed to process each frame. For this reason supporting large batch sizes and reporting performance when using large batch sizes can be completely reasonable. This is not at all the case for applications which require real-time inference like self driving cars and augmented reality however. For these applications, waiting to group multiple frames into a batch is unacceptable since a result is expected for each frame soon after being received. So, “average time per frame” is only equal to real-time latency if the batch size is equal to 1. The Project Brainwave paper has some great details on this.
This confusion between latency and throughput is only one of many issues that can crop up when comparing the metrics for one deep learning accelerator over the other. For more details, see slide 20-25 of this excellent presentation.
Avoid All Complexity
Exploiting sparsity, model compression, systolic arrays, or temporal redundancy seems hard. Avoid all this hassle by telling everyone that you’ll support these features but in the end just do the bare minimum.
Why is this so bad?Choosing where your device falls on the specialization-generalization trade-off curve is a critical decision. You’ve already chosen to build hardware specifically for neural network computation, so go ahead and specialize! Without any specific optimizations for your application of choice your system will be no better than a general purpose CPU or GPU.
The Yield Will Get Better, I Promise
The device physics folks down the hall can surely scale their experimental technology to production level soon. CMOS is old news. Its all about graphene, spintronics, optical computing, or <insert your own post-CMOS tech here>.
Why is this so bad?This advice can go for pretty much any kind of computer architecture research but is especially relevant for deep learning hardware due to its popularity. Computer architects often get really excited when they find themselves speaking with device physics people. New computing technologies sound very promising and have all kinds of desirable properties. The challenge with nearly all of these new technologies however is scaling the process from tens of devices per chip to millions or billions of devices per chip. If you are a process design engineer or a device physics person yourself then by all means do research in that field, but if you are just a hardware engineer then be wary of any technology that isn’t being used at an industrial scale. Otherwise you’ll find yourself putting bogus numbers in your architectural simulator and/or never be able to fabricate your chip.
Listen, if we wanted to write software we would have joined Facebook. Let’s just have one engineer extend gcc to compile to our instruction set so that we aren’t technically lying when we say that you can write code for our chip.
Why is this so bad?I hope this goes without saying, but hardware is useless without software support! Hardware designers can get dismissive about software simply because it isn’t their area of expertise but they really shouldn’t. With a few exceptions here and there, even application specific hardware must be programmed. Any pitch for a new piece of hardware must include both the exciting new features that the hardware provides as well as an in-depth description of the software stack used to program the hardware. The features are why someone might be interested in the hardware and the software stack is how someone might use the hardware to leverage those great features. Because hardware designers are the most comfortable with the complexities of their hardware, they should be actively involved in the development of the software stack for their system.
I hope this helps you develop a useless deep learning chip of your very own! Did I miss any hardware design sins? If you can think of any, feel free to let me know in the comments below. 😄