Optical interconnect specialist Ayar Labs has announced that it is working with Nvidia, a leader in artificial intelligence (AI) and machine learning silicon, systems and software.
In February Ayar Labs announced a strategic collaboration with the world’s leading high-performance computing (HPC) firm, Hewlett Packard Enterprise (HPE).
Charles Wuischpard
Both Nvidia and HPE were part of the Series C funding worth $130 million that Ayar Labs secured in April.
Work partnerships
Ayar Labs has chiplet and external laser source technologies that enable optical input-output (I/O) suited for AI and high-performance computing markets.
Charles Wuischpard, CEO of Ayar Labs, says the work with HPE and Nvidia share common characteristics.
HPE is interested in optical interfaces for high-performance computing fabrics and, in particular, future generations of its Slingshot technology.
Nvidia is also interested in fabrics with its Mellanox technology, but its chips also impact the server. Wuishchpard describes its work with Nvidia as optically enabling Nvidia’s NVLink, its graphics processing unit (GPU) interface.
Nvidia’s optical needs
Bill Dally, chief scientist and senior vice president of research at Nvidia, outlined the company’s interest in optical interconnect at the OFC conference, held in San Diego in March.
Dally started by quantifying the hierarchy of bandwidths and power requirements when sending a bit in computing systems.
The maximum bandwidth and lowest power needs occur, not surprisingly, when data is sent on-chip, between the chip’s processing elements.
With each hierarchical connection jump after that - between chips on an interposer hosting, for example, GPUs and memory (referred to as a module), between modules hosted on a printed circuit board (PCB), linking the boards in a cabinet, and connecting cabinets in a cluster - the bandwidth drops (dubbed bandwidth tapering) and more power is needed to transmit a bit.
There are also different technologies used for the jumps: electrical traces connect the modules on the PCB; electrical cables link the boards in a cabinet (1m to 3m), while active optical cables link the cabinets (5m to 100m).
One issue is that electrical signalling is no longer getting faster (the FO4 delay metric is now constant) with each new CMOS process node. Another issue is that the electrical reach is shrinking with each signalling speed hike: 50-gigabit signals can span 3m, while 200-gigabit signals can span 1m.
Co-packaged optics, where optics are placed next to the IC, promises the best of both worlds: bettering the metrics of PCBs and electrical cable while matching the reach of active optical cables.
Co-packaged optics promises a 5x saving in power when sending a bit compared to a PCB trace while costing a tenth of an active optical cable yet matching its 100m reach. Co-packaged optics also promises a fourfold increase in density (bit/s/mm) compared to PCB traces, says Nvidia.
However, meeting these targets requires overcoming several challenges.
One is generating efficient lasers that deliver aligned frequency grids. Another is getting the micro-ring resonators, used for modulating the data over WDM links, to work reliably and in volume. Nvidia plans to use 8 or 16 micro-ring resonators per WDM link and has developed five generations of test chips that it is still evaluating.
Another issue is packaging the optics, reducing the optical loss when coupling the fibre to the GPU while avoiding the need for active alignment. Cost is a big unknown, says Dally, and if co-packaged optics proves significantly more costly than an electrical cable, it will be a non-starter.
Nvidia outlined an example optical link using 8- or 16-channel WDM links, each channel at 25 gigabit-per-second (Gbps), to enable 200 and 400-gigabit optical links.
Using two polarisations, 800-gigabit links are possible while upgrading each lambda to 50Gbps, and link speed doubles again to 1.6 terabits.
Implementing such links while meeting the cost, power, density and reach requirements is why Nvidia has invested in and is working with Ayar Labs.
“Nvidia has been keeping an eye on us for some time, and they are generally big believers in a micro-ring WDM-based architecture with a remote light source,” says Wuishchpard.
Nvidia is optimistic about overcoming the challenges and that in the coming years - it won’t say how many - it expects electrical signalling to be used only for power. At the same time, co-packaged optics will handle the interconnect.
Nvidia detailed a conceptual GPU architecture using co-packaged optics.
Each GPU would be co-packaged with two optical engines, and two GPUs would sit on a card. Eight or nine cards would fill a chassis and eight to 10 chassis a cabinet.
Each GPU cabinet would then connect to a switch cabinet which would host multiple switch chips, each switch IC co-packaged with six optical engines.
The resulting cluster would have 4,000 to 8,000 GPUs, delivering a ‘flat bandwidth taper’.
HPE’s roadmap
Ayar Labs is collaborating with HPE to develop optical interconnect technology for high-performance computing while jointly developing an ecosystem for the technology.
Marten Terpstra
“This is not just a component that you stick on, and your product becomes better and cheaper,” says Marten Terpstra, senior director of product management and high-performance networks at HPE. “This is a change in architecture.”
HPE is interested in Ayar Labs’ optical interconnect chiplets and lasers for upcoming generations of its Slingshot interconnect technology used for its ‘Shasta ‘ HPE Cray EX and other platforms.
The increase in signalling speeds from 50 to 100 gigabits and soon 200 gigabits is making the design of products more complicated and expensive in terms of cost, power and cooling.
“This [optical interconnect] is something you need to prepare for several years in advance,” says Terpstra. “It is a shift in how you create connectivity, an architectural change that takes time.”
Shasta architecture
HPE’s Slingshot interconnect is part of the liquid-cooled Shasta and a top-of-rack switch for air-cooled HPE Cray supercomputers and HPC clusters.
“There are two parts to Slingshot: the Rosetta chipset which sits inside the switch, and the Cassini chipset which sits inside a NIC [network interface controller] on the compute nodes,” says Terpstra.
The Shasta architecture supports up to 279,000 nodes, and any two endpoints can talk to each with a maximum of three hops.
The Shasta platform is designed to have a 10-year lifespan and has been built to support several generations of signalling.
The Rosetta is a 12.8Tbps (64x200Gbps) switch chipset. Terpstra points out that the topology of the switching in high-performance computing differs from that found in the data centre, such that the switch chip needs upgrading less frequently.
Shasta uses a dragonfly topology which is more distributed, whereas, in the data centre, the main aggregation layer distributes tremendous amounts of end-point traffic.
HPE is working on upgrading the Slingshot architecture but says endpoint connectivity is not growing as fast as the connectivity between the switches.
“We are driven by the capabilities of PCI Express (PCIe) and CXL and how fast you can get data in and out of the different endpoints,” says Terpstra. “The connectivity to the endpoints is currently 200 gigabits, and it will go to 400 and 800 gigabits.”
PCIe 6.0 is still a few years out, and it will support about 800 gigabits.
“The network as we know it today - or the fabric - is our current means by which we connect endpoints,” says Terpstra. “But that definition of endpoints is slowly morphing over time.”
A traditional endpoint compromises a CPU, GPU and memory, and there is a transition between the buses or interfaces such as PCIe, HDMI or NVLink to such networking protocols as Ethernet or Infiniband.
“That transition between what is inside and what is outside a compute node, and the networking that sits in between, that will become way more grey in the next few generations,” says Terpstra.
HPE’s interest in Ayar Labs’ optical interconnect technology is for both Slingshot and disaggregated architectures, the connectivity to the endpoint and the types of disaggregated endpoints built. So, for example, linking GPUs, linking CPUs, and also GPU-to-memory connections.
And just as with Nvidia’s designs, such connections have limitations in power, distance and cost.
“This kind of [optical input-output] technology allows you to overcome some of these limitations,” says Terpstra. “And that will become a part of how we construct these systems in the next few years.”
Ayar Labs’ work with both Nvidia and HPE has been ongoing since the year-start.
Funding
How will Ayar Labs be using the latest funding?
“Well, I can make payroll,” quips Wuischpard.
The funding will help staff recruitment; the company expects to have 130 staff by year-end. It will also help with manufacturing and issues such as quality and testing.
The start-up has orders this year to deliver thousands of units that meet certain specification and quality levels. “Samples to thousands of units is probably harder than going from thousands to tens of thousands of units,” says Wuischpard.
The company also has other partnerships in the pipeline, says Wuischpard, that it will announce in future.