Thursday, November 15, 2018 at 11:44AM

Start-up Habana Labs has developed a chip architecture that promises to speed up the execution of machine-learning tasks.

The Israeli start-up came out of secrecy in September to announce two artificial intelligence (AI) processor chips. One, dubbed Gaudi, is designed to tackle the training of large-scale neural networks. The chip will be available in 2019.

Eitan MedinaGoya, the start-up’s second device, is an inference processor that implements the optimised, trained neural network.

The Goya chip is already in prospective customers’ labs undergoing evaluation, says Eitan Medina, Habana’s chief business officer.

Habana has just raised $75 million in a second round of funding, led by Intel Capital. Overall, the start-up has raised a total of $120 million in funding.

Deep learning

Deep learning in a key approach used to perform machine learning. To perform deep learning, use is made of an artificial neural network with many hidden layers. A hidden layer is a layer of nodes found between the neural network’s input and output layers.

To benefit from deep learning, the neural network must first be trained with representative data. This is an iterative and computationally-demanding process.

The computing resources used to train the largest AI jobs has been doubled every 3.5 months since 2012

Once trained, a neural network is ready to analyse data. Common examples where trained neural networks are used include image classification and for autonomous vehicles.

Source: Habana Labs

Two types of silicon are used for deep learning: general-purpose server CPUs such as from Intel and graphics processing units (GPUs) from the likes of Nvidia.

Most of the growth has been in the training of neural networks and this is where Nvidia has done very well. Nvidia has a run rate close to $3 billion just building chips to do the training of neural networks, says Karl Freund, senior analyst, HPC and deep learning at Moor Insights & Strategy. “They own that market.”

Now custom AI processors are emerging from companies such as Habana that are looking to take business from Nvidia and exploit the emerging market for inference chips.

“Use of neural networks outside of the Super Seven [hyperscalers] is still a nascent market but it could be potentially a $20 billion market in the next 10 years,” says Freund. “Unlike in training where you have a very strong incumbent, in inference - which could be a potentially larger market - there is no incumbent.”

This is where many new chip entrants are focussed. After all, it is a lot easier to go after an emerging market than to displace a strong competitor such as Nvidia, says Freund, who adds that Nvidia has its own inference hardware but it is suited to solving really difficult problems such as autonomous vehicles.

“For any new processor architecture to have any justification, it needs to be significantly better than previous ones,” says Medina.

Habana cites the ResNet-50 image classification algorithm to highlight its silicon’s merits. ResNet-50 refers to a 50-layer neural network that makes use of a technique called residual learning that improves the efficacy of image classification.

Habana’s Goya HL-1000 processor can classify 15,000 images-per-second using ResNet-50 while Nvidia’s V100 GPU classifies 2,657and Intel’s dual-socket Platinum 8180 CPU achieves 1225 images-per-second.

“What we have architected is fundamentally better than CPUs and GPUs in terms of processing performance and the processing-power factor,” says Medina.

“Habana appears to be one of the first start-ups to bring an AI accelerator to the market, that is, to actually deliver a product for sale,” says Linley Gwennap, president and principal analyst of The Linley Group.

Both Habana and start-up Graphcore expect to have final products for sale this year, he says, while Wave Computing, another start-up, expects to enter production early next year.

“It is also impressive that Habana is reporting 5-6x better performance than Nvidia, whereas Graphcore’s lead is less than 2x,” says Gwennap. “Graphcore focuses on training, however, whereas the Goya chip is for inference.”

Habana appears to be one of the first start-ups to bring an AI accelerator to the market





Gaudi training processor

Habana’s Gaudi chip is a neural-network training processor. Once trained, the neural network is optimised and loaded into the inference chip such as Habana’s Goya to implement what has been learnt.

“The process of getting to a trained model involves a very different compute, scale-out and power-envelopment environment to that of inference,” says Medina.

To put this in perspective, the computing resources used to train the largest AI jobs has been doubled every 3.5 months since 2012. The finding, from AI research company OpenAI, means that the computing power being employed now has grown by over one million times since 2012.

Habana remains secretive about the details of its chips. It has said that the 16nm CMOS Gaudi chip can scale to thousands of units and that each device will have 2 terabits of input-output (I/O). This contrasts with GPUs used for training that do have scaling issues, it says.

First, GPUs are expensive and power-hungry devices. The data set used for training such as for image classification needs to be split across the GPUs. If the number of images - the batch size - given to each one is too large, the training model may not converge. If the model doesn't converge, the neural network will not learn to do its job.

In turn, reducing the batch size affects the overall throughput. “GPUs and CPUs want you to feed them with a lot of data to increase throughput,” says Medina.

Habana says that unlike GPUs, its training processor’s performance will scale with the number of devices used.

“We will show with the Gaudi that we can scale performance linearly,” says Medina. “Training jobs will finish faster and models could be much deeper and more complex.”

The Goya IC architecture. Habana says this is a general representation of the chip and what is shown is not the actual number of tensor processor cores (TPCs). Source: Habana Labs

Goya inference processor

The Goya processor comprises multiple tensor processor cores (TPCs), see diagram. Habana is not saying how many but each TPC is capable of processing vectors and matrices efficiently using several data types - eight-, 16- and 32-bit signed and unsigned integers and 32-bit floating point. To achieve this, the architecture used for the TPC is a very-long-instruction-word, (VLIW) single-instruction, multiple-data (SIMD) vector processor. Each TPC also has its own local memory.

Other on-chip hardware blocks include a general-purpose engine (GEMM), shared memory, an interface to external DDR4 SDRAM memory and support for PCI Express (PCIe) 4.0.

What we have architected is fundamentally better than CPUs and GPUs in terms of processing performance and the processing-power factor

Habana claims its inference chip has a key advantage when it comes to latency, the time it takes for the inference chip to deliver its answer.

Latency too is a function of the batch size - the number of jobs - presented to the device. Being able to pool jobs presented to the chip is a benefit but not if it exceeds the latency required.

“If you listen to what Google says about real-time applications, to meet the 99th percentile of real-time user interaction, they need the inference to be accelerated to under 7 milliseconds,” says Medina. “Microsoft also says that latency is incredibly important and that is why they can’t use a batch size of 64.”

Habana and other entrants are going after applications where their AI processors are efficient at real-time tasks with a batch size of one. “Everyone is focussing on what Nvidia can’t do well so they are building inference chips that do very well with low batch sizes,” says Freund.

Having a low-latency device not only will enable all sorts of real-time applications but will also allow a data centre operator to rent out the AI processor to multiple customers, knowing what the latency will be for each job.

“This will generate more revenue and lower the cost of AI,” says Medina.

AI PCIe cards

Habana is offering two PCIe 4.0 card versions of its Goya chip: one being one-slot wide and the second being double width. This is to conform to some customers that already use platforms with double-width GPU cards.

Habana’s PCIe 4.0 card includes the Goya chip and external memory and consumes around 100W, the majority of the power consumed by the inference chip.

The card’s PCIe 4.0 interface has 16 lanes (x16) but nearly all the workloads can manage with a single lane.

“The x16 is in case you go for more complicated topologies where you can split the model between adjacent cards and then we need to pass information between our processors,” says Medina.

Here, a PCIe switch chip would be put on the motherboard to enable the communications between the Goya processors.

Do start-ups have a sustainable architectural roadmap that offers innovation beyond just such single-cycle operations?

Applications

Habana has developed demonstrations of four common applications to run on the Goya cards. These include image classification, machine translation, recommendations, and the classification of text known as sentiment analysis.

The four were chosen as potential customers want to see these working. “If they are going to buy your hardware for inference, they want to make sure it can deal with any topology they come up with in future,” says Medina.

Habana says it is already engaged with customers other than the largest data centre operators. And with time, the start-up expects to develop inference chips with tailored I/O to address dedicated applications such as autonomous vehicles.

There are also other markets emerging beside data centres and self-driving cars.

“Mythic, for example, targets security cameras while other start-ups offer IP cores, and some target the Internet of Things and other low-cost applications,” says Gwennap. “Eventually, most processors will have some sort of AI accelerator built-in, so there are many different opportunities for this technology.”

Start-up challenge

The challenge facing all the AI processor start-ups, says Freund, is doing more thandeveloping an architecture that can do a multiply-accumulate operation in a single processor clock cycle, and not just with numbers but withn-dimensional matrices.

“That is really hard but eventually - give or take a year - everyone will figure it out,” says Freund.

The question for the start-ups is: do they have a sustainable architectural roadmap that offers innovation beyond just such single-cycle operations?

“What architecturally are you able to do beyond that to avoid being crushed by Nvidia, and if not Nvidia then Intel because they haven't finished yet,” says Freund.

This is what all these start-ups are going to struggle with whereas Nvidia has 10,000 engineers figuring it out, he warns.

Article updated on Nov 16 to report the latest Series B funding.