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AMD’s AI strategy comes into view with Xilinx, GPU, software plans

Chip designer hopes to have broad inference and training coverage from the edge to the cloud

Dylan Martin Mon 13 Jun 2022 // 19:30 UTC

Analysis After re-establishing itself in the datacenter over the past few years, AMD is now hoping to become a big player in the AI compute space with an expanded portfolio of chips that cover everything from the edge to the cloud.
It's quite an ambitious goal, given Nvidia's dominance in the space with its GPUs and the CUDA programming model, plus the increasing competition from Intel and several other companies.
But as executives laid out during AMD's Financial Analyst Day 2022 event last week, the resurgent chip designer believes it has the right silicon and software coming into place to pursue the wider AI space.

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Nvidia wants to lure you to the Arm side with fresh server bait

GPU giant promises big advancements with Arm-based Grace CPU, says the software is ready

Dylan Martin Fri 17 Jun 2022 // 19:17 UTC

Interview 2023 is shaping up to become a big year for Arm-based server chips, and a significant part of this drive will come from Nvidia, which appears steadfast in its belief in the future of Arm, even if it can't own the company.
Several system vendors are expected to push out servers next year that will use Nvidia's new Arm-based chips. These consist of the Grace Superchip, which combines two of Nvidia's Grace CPUs, and the Grace-Hopper Superchip, which brings together one Grace CPU with one Hopper GPU.
The vendors lining up servers include American companies like Dell Technologies, HPE and Supermicro, as well Lenovo in Hong Kong, Inspur in China, plus ASUS, Foxconn, Gigabyte, and Wiwynn in Taiwan are also on board. The servers will target application areas where high performance is key: AI training and inference, high-performance computing, digital twins, and cloud gaming and graphics.

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AMD touts big datacenter, AI ambitions in CPU-GPU roadmap

Epyc future ahead, along with Instinct, Ryzen, Radeon and custom chip push

Dylan Martin Fri 10 Jun 2022 // 01:58 UTC

After taking serious CPU market share from Intel over the last few years, AMD has revealed larger ambitions in AI, datacenters and other areas with an expanded roadmap of CPUs, GPUs and other kinds of chips for the near future.
These ambitions were laid out at AMD's Financial Analyst Day 2022 event on Thursday, where it signaled intentions to become a tougher competitor for Intel, Nvidia and other chip companies with a renewed focus on building better and faster chips for servers and other devices, becoming a bigger player in AI, enabling applications with improved software, and making more custom silicon.
"These are where we think we can win in terms of differentiation," AMD CEO Lisa Su said in opening remarks at the event. "It's about compute technology leadership. It's about expanding datacenter leadership. It's about expanding our AI footprint. It's expanding our software capability. And then it's really bringing together a broader custom solutions effort because we think this is a growth area going forward."

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Ditching VMware over the Broadcom buy? Here are some of your options

What's your contingency plan?

Tobias Mann Tue 28 Jun 2022 // 18:01 UTC

Opinion Broadcom has yet to close the deal on taking over VMware, but the industry is already awash with speculation and analysis as to how the event could impact the cloud giant's product availability and pricing.
If Broadcom's track record and stated strategy tell us anything, we could soon see VMware refocus its efforts on its top 600 customers and raise prices, and leave thousands more searching for an alternative.
The jury is still out as to whether Broadcom will repeat the past or take a different approach. But, when it comes to VMware's ESXi hypervisor, customer concern is valid. There aren't many vendor options that can take on VMware in this arena, Forrester analyst Naveen Chhabra, tells The Register.

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Microsoft promises to tighten access to AI it now deems too risky for some devs

Deep-fake voices, face recognition, emotion, age and gender prediction ... A toolbox of theoretical tech tyranny

Katyanna Quach Tue 21 Jun 2022 // 22:54 UTC

Microsoft has pledged to clamp down on access to AI tools designed to predict emotions, gender, and age from images, and will restrict the usage of its facial recognition and generative audio models in Azure.
The Windows giant made the promise on Tuesday while also sharing its so-called Responsible AI Standard, a document [PDF] in which the US corporation vowed to minimize any harm inflicted by its machine-learning software. This pledge included assurances that the biz will assess the impact of its technologies, document models' data and capabilities, and enforce stricter use guidelines.
This is needed because – and let's just check the notes here – there are apparently not enough laws yet regulating machine-learning technology use. Thus, in the absence of this legislation, Microsoft will just have to force itself to do the right thing.

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Is computer vision the cure for school shootings? Likely not

Gun-detecting AI outfits want to help while root causes need tackling

Brandon Vigliarolo Tue 28 Jun 2022 // 18:58 UTC

Comment More than 250 mass shootings have occurred in the US so far this year, and AI advocates think they have the solution. Not gun control, but better tech, unsurprisingly.
Machine-learning biz Kogniz announced on Tuesday it was adding a ready-to-deploy gun detection model to its computer-vision platform. The system, we're told, can detect guns seen by security cameras and send notifications to those at risk, notifying police, locking down buildings, and performing other security tasks.
In addition to spotting firearms, Kogniz uses its other computer-vision modules to notice unusual behavior, such as children sprinting down hallways or someone climbing in through a window, which could indicate an active shooter.

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Cerebras sets record for 'largest AI model' on a single chip

Plus: Yandex releases 100-billion-parameter language model for free, and more

Katyanna Quach Mon 27 Jun 2022 // 04:16 UTC

In brief US hardware startup Cerebras claims to have trained the largest AI model on a single device powered by the world's largest Wafer Scale Engine 2 chip the size of a plate.
"Using the Cerebras Software Platform (CSoft), our customers can easily train state-of-the-art GPT language models (such as GPT-3 and GPT-J) with up to 20 billion parameters on a single CS-2 system," the company claimed this week. "Running on a single CS-2, these models take minutes to set up and users can quickly move between models with just a few keystrokes."
The CS-2 packs a whopping 850,000 cores, and has 40GB of on-chip memory capable of reaching 20 PB/sec memory bandwidth. The specs on other types of AI accelerators and GPUs pale in comparison, meaning machine learning engineers have to train huge AI models with billions of parameters across more servers.

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Samsung invests in ML chip startup NeuReality

Coining the term hardware-based 'AI hypervisor' has to be worth several million, dontcha think?

Dylan Martin Tue 21 Jun 2022 // 23:28 UTC

The venture capital arm of Samsung has cut a check to help Israeli inference chip designer NeuReality bring its silicon dreams a step closer to reality.
NeuReality announced Monday it has raised an undisclosed amount of funding from Samsung Ventures, adding to the $8 million in seed funding it secured last year to help it get started.
As The Next Platform wrote in 2021, NeuReality is hoping to stand out with an ambitious system-on-chip design that uses what the upstart refers to as a hardware-based "AI hypervisor."

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If AI chatbots are sentient, they can be squirrels, too

Plus: FTC warns against using ML for automatic content moderation, and more

Katyanna Quach Mon 20 Jun 2022 // 15:00 UTC

In Brief No, AI chatbots are not sentient.
Just as soon as the story on a Google engineer, who blew the whistle on what he claimed was a sentient language model, went viral, multiple publications stepped in to say he's wrong.
The debate on whether the company's LaMDA chatbot is conscious or has a soul or not isn't a very good one, just because it's too easy to shut down the side that believes it does. Like most large language models, LaMDA has billions of parameters and was trained on text scraped from the internet. The model learns the relationships between words, and which ones are more likely to appear next to each other.

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GPUs aren’t always your best bet, Twitter ML tests suggest

Graphcore processor outperforms Nvidia rival in team's experiments

Tobias Mann Thu 16 Jun 2022 // 00:25 UTC

GPUs are a powerful tool for machine-learning workloads, though they’re not necessarily the right tool for every AI job, according to Michael Bronstein, Twitter’s head of graph learning research.
His team recently showed Graphcore’s AI hardware offered an “order of magnitude speedup when comparing a single IPU processor to an Nvidia A100 GPU,” in temporal graph network (TGN) models.
“The choice of hardware for implementing Graph ML models is a crucial, yet often overlooked problem,” reads a joint article penned by Bronstein with Emanuele Rossi, an ML researcher at Twitter, and Daniel Justus, a researcher at Graphcore.

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