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Qualcomm-AI-research/bcresnet

Folders and files, repository files navigation, broadcasted residual learning for efficient keyword spotting..

This repository contains the implementation for the paper presented in

Byeonggeun Kim *1 , Simyung Chang *1 , Jinkyu Lee 1 , Dooyong Sung 1 , "Broadcasted Residual Learning for Efficient Keyword Spotting", Interspeech 2021. [ArXiv]

*Equal contribution 1 Qualcomm AI Research (Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.)

It contains the keyword spotting standard benchmark, Google speech command datasets v1 and v2 .

We propose a broadcasted residual learning method for keyword spotting that achieves high accuracy with small model size and computational load, making it well-suited for use on resource-constrained devices such as mobile phones. The method involves configuring most of the residual functions as 1D temporal convolutions while still allowing 2D convolutions via a broadcasted-residual connection that expands the temporal output to the frequency-temporal dimension. This approach enables the network to effectively represent useful audio features with much less computation than conventional convolutional neural networks. We also introduce a novel network architecture called the Broadcasting-residual network (BC-ResNet) that leverages this broadcasted residual learning approach, and we describe how to scale the model according to the target device's resources. BC-ResNets achieve state-of-the-art results, achieving 98.0% and 98.7% top-1 accuracy on Google speech command datasets v1 and v2, respectively. Our approach consistently outperforms previous approaches while using fewer computations and parameters.

Getting Started

Prerequisites.

This code requires the following:

• python >= 3.6
• pytorch >= 1.7.1

Installation

Here are some examples of how to use the code:

• To use BCResNet-8 with GPU 0 and GSC dataset v2, and download the dataset, run the following command:
• To use BCResNet-1 with GPU 1 and GSC dataset v1, and skip downloading the dataset, run the following command:

The downloaded dataset will be saved to the data/ directory by default.

If you find our work useful for your research, please cite the following:

Contributors 2

• Python 100.0%

• Documentation

AI Model Efficiency Toolkit (AIMET)

pruning, quantization, network-compression, automl, deep-neural-networks, network-quantization, model-efficiency, open-source.

Open-sourcing our AI Model Efficiency Toolkit

Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware accelerators.

Why AI Model Efficiency Toolkit?

Performance:.

Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. However, often when quantizing a machine learning model (e.g., from 32-bit floating point to an 8-bit fixed point value), the model accuracy is sacrificed. AIMET solves this problem through novel techniques like data-free quantization that provides state of the art INT8 results as shown in Data-Free Quantization paper ( ICCV’19).

Scalability:

How does it work.

• Quantization
• Cross-Layer Equalization Equalize weight tensors to reduce amplitude variation across channels
• Bias Correction Corrects shift in layer outputs introduced due to quantization
• Quantization Simulation Simulate on-target quantized inference accuracy
• Fine-tuning Use quantization sim to train the model further to improve accuracy
• Compression
• Spatial SVD Tensor-decomposition technique to split a large layer into two smaller ones
• Channel Pruning Removes redundant input channels from a layer and reconstructs layer weights
• Automatic selection of per-layer compression ratios Automatically selects how much to compress each layer in the model
• Visualization
• Visualize weight ranges
• Visualize per-layer sensitivity to compression

What performance benefits can you expect?

Watch the videos below to find out how to get the most out of the ai model efficiency toolkit.

Explore related Qualcomm AI Research papers

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Check out related blog posts from Qualcomm AI Research:

• Introducing AI Model Efficiency Toolkit (blogpost)
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• New research on quantization could revolutionize power-efficient AI
• Qualcomm® Neural Processing SDK for AI

Check out the documentation:

• AI Model Efficiency Toolkit User Guide
• AI Model Efficiency Toolkit API Documentation
• AI Model Efficiency Toolkit Forum

Webinar: Generative AI at the edge

Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential.

In this webinar you’ll learn about:

• Why on-device AI is key
• Full-stack AI optimizations to make on-device AI possible and efficient
• Advanced techniques like quantization, distillation, and speculative decoding
• How generative AI models can be run on device and examples of some running now
• Qualcomm Technologies’ role in scaling on-device generative AI

Qualcomm AI Research is an initiative of Qualcomm Technologies, Inc.

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September 17th: Day 1 – Keynote & Presentations 8:00am-4:00pm Location: Qualcomm – N Auditorium 5775 Morehouse Drive, San Diego, CA September 17th: 4:30pm-6:30pm: Networking Meetings & Reception Location: La Jolla Marriott 4240 La Jolla Drive, San Diego, CA September 18th: Day 2: 8:30am – 12:30pm Presentations & Meetings Location: La Jolla Mariott 4240 La Jolla Drive, San Diego, CA

Dr. Joseph Soriaga

Senior Director of Technology, Qualcomm AI Research Dr. Joseph Soriaga is a Senior Director of Technology and leads the core team within Qualcomm AI Research that is broadly responsible for driving hardware and software platform innovations, advancing applications in areas such as mobile, automotive, XR and wireless, and contributing to fundamental research in machine learning and generative AI. Prior to this role, he led 5G core physical layer research team within Qualcomm Wireless R&D and helped drive the standardization of 5G as a both a 3GPP RAN1 delegate and a technical specifications editor. He has also had previous experience as systems engineering lead for Qualcomm’s 3G EV-DO commercial cell-site modem (CSM). He received his BSEE from Caltech, and PhD ECE from UCSD with focus on coding theory, communications, and information theory. He was awarded IEEE Data Storage Best Paper in 2007 and the EURASIP Journal on Wireless Communications and Networking Best Paper in 2012, and he has been granted over 280 US Patents.

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Stable-diffusion-v2.1, state-of-the-art generative ai model used to generate detailed images conditioned on text descriptions..

Generates high resolution images from text prompts using a latent diffusion model. This model uses CLIP ViT-L/14 as text encoder, U-Net based latent denoising, and VAE based decoder to generate the final image.

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Ampere Computing pairs with Qualcomm on AI, unveils new chip

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Among AI infrastructure hopefuls, Qualcomm has become an unlikely ally

The enemy of my enemy is my best friend.

Analysis With its newly formed partnership with Arm server processor designer Ampere Computing, Qualcomm is slowly establishing itself as AI infrastructure startups' best friend.

Announced during Ampere's annual strategy and roadmap update on Thursday , the duo promised a 2U machine that includes eight Qualcomm AI 100 Ultra accelerators for performing machine-learning inference and 192 Ampere CPU cores. "In a typically 12.5kW rack, this equates to hosting up to 56 AI accelerators with 1,344 computation cores, while eliminating the need for expensive liquid cooling," Ampere beamed.

Ampere boosts core count to 256 with latest SKUs

Ampere also confirmed its latest server processor would feature 256 CPU cores and up to 12 memory channels. This new chip, due to arrive next year, builds on the Ampere custom Arm architecture introduced last year, and will see it transition to TSMC's 3nm process tech.

Check out our sibling site The Next Platform for more on Ampere's 256-core beasty.

Ampere and its partner Oracle have gone to great lengths to demonstrate that running the large language models (LLMs) behind many popular chatbots is entirely possible on CPUs, provided you set your expectations appropriately. We've explored this concept at length, but in a nutshell limited memory bandwidth means that CPUs are generally best suited to running smaller models between seven and eight billion parameters in size and usually only at smaller batch sizes — that is to say fewer concurrent users.

This is where Qualcomm's AI 100 accelerators come in, as their higher memory bandwidth allows them to handle inferencing on larger models or higher batch sizes. And remember that inferencing involves running operations over the whole model; if your LLM is 4GB, 8GB, or 32GB in size, that's a lot of numbers to repeatedly crunch every time you want to generate the next part of a sentence or piece of source code from a prompt.

Why Qualcomm?

When it comes to AI chips for the datacenter, Qualcomm isn't a name that tends to come up all that often.

Most of the focus falls on GPU giant Nvidia with the remaining attention split between Intel's Gaudi and AMD's Instinct product lines. Instead, most of the attention Qualcomm has garnered has centered around its AI smartphone and notebook strategy.

However, that's not to say Qualcomm doesn't have a presence in the datacenter. In fact, its AI 100 series accelerators have been around for years, with its most recent Ultra-series parts making their debut last fall.

The accelerator is a slim, single slot PCIe card aimed at inferencing on LLMs. At 150W the card's power requirements are rather sedate compared to the 600W and 700W monsters from AMD and Nvidia that are so often in the headlines.

Despite its slim form factor and relatively low-power draw, Qualcomm claims a single AI 100 Ultra is capable of running 100 billion parameter models while a pair of them can be coupled to support GPT-3 scale models (175 billion parameters).

In terms of inference performance, the 64-core card pushes 870 TOPs [ PDF ] at INT8 precision and is fueled by 128GB of LPDDR4x memory capable of 548GB/s of bandwidth.

Memory bandwidth is a major factor for scaling AI inferencing to larger batch sizes.

Generating the first token which, with chatbots we experience as the delay between submitting a prompt and the first word of the response appearing, is often compute bound. However, beyond that, each subsequent word generated tends to be memory bound.

This is part of the reason that GPU vendors like AMD and Nvidia have been moving to larger banks of faster HBM3 and HBM3e memory. The two silicon slingers' latest chips boast memory bandwidths in excess of 5TB/s, roughly ten times that of Qualcomm's part.

To overcome some of these limitations, Qualcomm has leaned heavily on software optimizations , adopting technologies like speculative decoding and micro-scaling formats (MX).

If you're not familiar, speculative decoding uses a small, lightweight model to generate the initial response and then uses a larger model to check and correct its accuracy. In theory, this combination can boost the throughput and efficiency of an AI app.

Formats like MX6 and MX4, meanwhile, aim to reduce the memory footprint of models. These formats are technically a form of quantization that compresses model weights to lower precision, reducing the memory capacity and bandwidth required.

By combining MX6 and speculative decoding, Qualcomm claims these technologies can achieve a fourfold improvement in throughput over a FP16 baseline.

For Ampere, Qualcomm offers an alternative to Nvidia GPUs, which already work with its CPUs, for larger scale AI inferencing.

AI upstarts amp Qualcomm's accelerators

Ampere isn't the only one that's teamed up with Qualcomm to address AI inferencing. There's a missing piece to this puzzle that hasn't been addressed: Training.

Waferscale AI startup Cerebras, another member of Ampere's AI Platform Alliance, announced a collaboration with Qualcomm back in March alongside the launch of its WSE-3 chips and CS-3 systems.

Cerebras is unique among AI infrastructure vendors for numerous reasons, the most obvious being their chips are literally the size of dinner plates and now each boast 900,000 cores and 44GB of SRAM — and no, that's not a typo.

As impressive as Cerebra's waferscale chips may be, they're designed for training models, not running them. This isn't as big a headache as it might seem. Inferencing is a far less vendor-specific endeavor than training. This means that models trained on Cerebra's CS-2 or 3 clusters can be deployed on any number of accelerators with minimal tuning.

The difference with Qualcomm is that the two are making an ecosystem play. As we covered at the time, Cerebras is working to train smaller, more accurate, and performant models that can take full advantage of Qualcomm's software optimizations around speculative decoding, sparse inference, and MX quantization.

Building the ecosystem

Curiously, Qualcomm isn't listed as a member of the AI Platform Alliance, at least not yet anyway. Having said that, the fact that Qualcomm's AI 100 Ultra accelerators are already on the market may mean they're just a stop gap while other smaller players within the alliance catch up.

And in this regard, the AI Platform Alliance has a number of members working on inference accelerators at various stages of commercialization. One of the more interesting we've come across is Furiosa — and yes, that is a Mad Max reference. The chip startup even has a computer vision accelerator codenamed Warboy, if there was any doubt.

Furiosa's 2nd-gen accelerator codenamed RNGD — pronounced Renegade because in the post-AI world, who needs vowels —  is fabbed on a TSMC 5nm process and boasts up to 512 teraFLOPS of 8-bit performance or 1,024 TOPS at INT4. So, for workloads that can take advantage of lower 4-bit precision, the 150W chip has a modest advantage over Qualcomm's AI 100.

The chip's real bonus is 48GB of HBM3 memory which, while lower in capacity than Qualcomm's part, boasts nearly three times more bandwidth at 1.5TB/s.

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• Aleph Alpha enlists Cerebras waferscale supers to train AI for German military

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Hugging face to make $10m worth of old nvidia gpus freely available to ai devs.

When we might see the RNGD in the wild remains to be seen. However, the key takeaway from the AI Platform Alliance seems to exist so that individual startups can focus on tackling whatever aspect of the AI spectrum they're best at and lean on the others for the rest, whether that's through direct collaborations or standardization.

 In the meantime, it seems Qualcomm has picked up a few new friends along the way.

Filling a gap

Ampere’s reliance on Qualcomm for larger models at higher batch sizes may be short lived, thanks to architectural improvements introduced in the Armv9 instruction set architecture.

As we previously reported, the custom cores the CPU vendor developed for its Ampere One family of processors utilized elements of both the older v8 and newer v9 architectures. As we understand it, the v9-A spec introduced Scalable Matrix Extension 2 (SME2) support aimed at accelerating the kinds of matrix mathematics common in machine learning workloads. However for the moment, we’re told Ampere’s current chips are handling AI inferencing jobs using its twin 128-bit vector units.

It's reasonable to believe future Arm-compatible chips from Ampere and others could make use of SME2. In fact, on the client side, Apple’s new M4 SoC is Armv9-compatible with SME2 acceleration baked into its cores, The Register has learned from trusted sources.

Qualcomm was actually one of the first to adopt Armv9, in some of its Snapdragon system-on-chips. However, the chip biz appears to be going back to Armv8, when using CPU designs from its Nuvia acquisition, a decision we have little doubt has become a point of contention with Arm. While Arm would like its customers to pick v9 with SME2 for CPU-based AI inference, Qualcomm is instead taking the line that v8 is fine with inference offloaded from the CPU to another processing unit.

In datacenter land, memory bandwidth will remain a bottleneck regardless of Armv9 or SME2. The introduction of speedier multiplexer combined rank (MCR) DIMMs should help, with 12 channel platforms capable of achieving 825GB/s of bandwidth.

As we’ve seen from Intel’s Xeon 6 demos, this bandwidth boost should allow models up to 70 billion parameters to run reasonably at 4-bit precision on a single CPU. ®

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Can Artificial Intelligence Make the PC Cool Again?

Microsoft, HP, Dell and others unveiled a new kind of laptop tailored to work with artificial intelligence. Analysts expect Apple to do something similar.

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By Karen Weise and Brian X. Chen

Karen Weise reported from Microsoft’s headquarters in Redmond, Wash., and Brian X. Chen from San Francisco.

The race to put artificial intelligence everywhere is taking a detour through the good old laptop computer.

Microsoft on Monday introduced a new kind of computer designed for artificial intelligence. The machines, Microsoft says, will run A.I. systems on chips and other gear inside the computers so they are faster, more personal and more private.

The new computers, called Copilot+ PC, will allow people to use A.I. to make it easier to find documents and files they have worked on, emails they have read, or websites they have browsed. Their A.I. systems will also automate tasks like photo editing and language translation.

The new design will be included in Microsoft’s Surface laptops and high-end products that run on the Windows operating system offered by Acer, Asus, Dell, HP, Lenovo and Samsung, some of the largest PC makers in the world .

The A.I. PC, industry analysts believe, could reverse a longtime decline in the importance of the personal computer. For the last two decades, the demand for the fastest laptops has diminished because so much software was moved into cloud computing centers. A strong internet connection and web browser was all most people needed.

But A.I. stretches that long-distance relationship to its limits. ChatGPT and other generative A.I. tools are run in data centers stuffed with expensive and sophisticated chips that can process the largest, most advanced systems. Even the most cutting-edge chatbots take time to receive a query, process it and send back a response. It is also extremely expensive to manage.

Microsoft wants to run A.I. systems directly on a personal computer to eliminate that lag time and cut the price. Microsoft has been shrinking the size of A.I. systems, called models, to make them easier to run outside of data centers. It said more than 40 will run directly on the laptops. The smaller models are generally not as powerful or accurate as the most cutting-edge A.I. systems, but they are improving enough to be useful to the average consumer.

“We are entering a new era where computers not only understand us, but can anticipate what we want and our intents,” said Satya Nadella, Microsoft’s chief executive, at an event at its headquarters in Redmond, Wash.

Analysts expect Apple to follow suit next month at its conference for software developers, where the company will announce an overhaul for Siri , its virtual assistant, and an overall strategy for integrating more A.I. capabilities into its laptops and iPhones.

Whether the A.I. PC takes off depends on the companies’ ability to create compelling reasons for buyers to upgrade. The initial sales of these new computers, which cost more than $1,000, will be small, said Linn Huang, an analyst at IDC, which closely tracks the market. But by the end of the decade — assuming A.I. tools turn out to be useful — they will be “ubiquitous,” he predicted. “Everything will be an A.I. PC.”

The computer industry is looking for a jolt. Consumers have been upgrading their own computers less frequently, as the music and photos they once stored on their machines now often live online, on Spotify, Netflix or iCloud. Computer purchases by companies, schools and other institutions have finally stabilized after booming — and then crashing — during the pandemic.

Some high-end smartphones have already been integrating A.I. chips, but the sales have fallen short because the features “are still not sophisticated enough to catalyze a faster upgrade cycle,” Mehdi Hosseini, an analyst at Susquehanna International Group, wrote in a research note. It will be at least another year, he said, before enough meaningful breakthroughs will lead consumers to take note.

At the event, Microsoft showed new laptops with what it likened to having a photographic memory. Users can ask Copilot, Microsoft’s chatbot, to use a feature called Recall to look up a file by typing a question using natural language, such as, “Can you find me a video call I had with Joe recently where he was holding an ‘I Love New York’ coffee mug?” The computer will then immediately be able to retrieve the file containing those details because the A.I. systems are constantly scanning what the user does on the laptop.

“It remembers things that I forget,” said Matt Barlow, Microsoft’s head of marketing for Surface computers, in an interview.

Microsoft said the information used for this Recall function was stored directly on the laptop for privacy, and would not be sent back to the company’s servers or be used in training future A.I. systems. Pavan Davuluri, a Microsoft executive overseeing Windows, said that with the Recall system users would also be able to opt out of sharing certain types of information, such as visits to a specific website, but that some sensitive data, such as financial information and private browsing sessions, would not be monitored by default.

Microsoft also demonstrated live transcripts that translate in real time, which it said would be available on any video that streams across a laptop’s screen.

Microsoft last month released A.I. models small enough to run on a phone that it said performed almost as well as GPT-3.5, the much larger system that initially underpinned OpenAI’s ChatGPT chatbot when it debuted in late 2022.

(The New York Times sued OpenAI and Microsoft in December for copyright infringement of news content related to A.I. systems.)

Chipmakers have also made advances, like adjusting a laptop’s battery life to allow for the enormous number of calculations that A.I. demands. The new computers have dedicated chips built by Qualcomm, the largest chip provider for smartphones.

Though the type of chip inside the new A.I. computers, known as a neural processing unit, specializes in handling complex A.I. tasks, such as generating images and summarizing documents, the benefits may still be unnoticeable to consumers, said Subbarao Kambhampati, a professor and researcher of artificial intelligence at Arizona State University.

Most of the data processing for A.I. still has to be done on a company’s servers instead of directly on the devices, so it’s still important that people have a fast internet connection, he added.

But the neural processing chips also speed up other tasks, such as video editing or the ability to use a virtual background inside a video call, said Brad Linder, the editor of Liliputing, a blog that has covered computers for nearly two decades. So, even if people don’t buy into the hype surrounding artificial intelligence, they may end up getting an A.I. computer for other reasons.

Karen Weise writes about technology and is based in Seattle. Her coverage focuses on Amazon and Microsoft, two of the most powerful companies in America. More about Karen Weise

Brian X. Chen is the lead consumer technology writer for The Times. He reviews products and writes Tech Fix , a column about the social implications of the tech we use. More about Brian X. Chen

Explore Our Coverage of Artificial Intelligence

News  and Analysis

News Corp, the Murdoch-owned empire of publications like The Wall Street Journal and The New York Post, announced that it had agreed to a deal with OpenAI to share its content  to train and service A.I. chatbots.

The Silicon Valley company Nvidia was again lifted by sales of its A.I. chips , but it faces growing competition and heightened expectations.

Researchers at the A.I. company Anthropic claim to have found clues about the inner workings  of large language models, possibly helping to prevent their misuse and to curb their potential threats.

The Age of A.I.

D’Youville University in Buffalo had an A.I. robot speak at its commencement . Not everyone was happy about it.

A new program, backed by Cornell Tech, M.I.T. and U.C.L.A., helps prepare lower-income, Latina and Black female computing majors  for A.I. careers.

Publishers have long worried that A.I.-generated answers on Google would drive readers away from their sites. They’re about to find out if those fears are warranted, our tech columnist writes .

A new category of apps promises to relieve parents of drudgery, with an assist from A.I.  But a family’s grunt work is more human, and valuable, than it seems.

TSMC-backed VisEra cautiously optimistic about 2024, with Longtan plant to reach full capacity in 3Q24

Credit: DIGITIMES

Optical film packaging and CMOS image sensor (CIS) back-end manufacturer VisEra held its shareholder meeting and board of directors election on May 22.

In a joint press conference after the meeting, VisEra chairman and president Robert Kuan pointed to strong demand for high-resolution, large-sized CIS, and that the company's Longtan plant may reach full production capacity in the third quarter of 2024. Price increases were another concern, with rumors stating that China-based CIS manufacturers would raise prices for high-end products by at least 10% in the first quarter.

In response, Kuan said VisEra will seek a win-win situation and is discussing price increases with its clients as production reaches full capacity. With order visibility reaching into the third quarter as originally projected, VisEra expects the latter half of 2024 to be better than the first half .

According to Kuan, fluctuations in the general economy led to 2023 revenue dropping to NT$7.237 billion (US$224 million), a 20% decline compared to the previous year. This was mainly the result of fluctuating smartphone demand and CIS inventory adjustments. However, as penetration rates for high-end and foldable smartphones increase, demand for high-resolution, large-sized CIS continues to rise, and thus Kuan expects 2024 revenue to surpass 2023 levels.

Moreover, Kuan noted that AI applications will further drive demand for high-resolution, large-sized CIS for image fusion and processing tasks. The larger screens of foldable phones will also drive CIS demand. Furthermore, the general trend toward higher resolutions and larger sensor sizes will lead to increased demand for chips.

TSMC will hold its technology symposium on May 23, and as one of TSMC's reinvested companies, VisEra will continue to collaborate with its parent on products such as 0.45-micron CIS, said Kuan. TSMC is reportedly developing two stacked image sensors as well. Kuan noted that as image sensors shrink in size, new optical architectures will be required to maintain image clarity.

In response to TSMC's plans for production in Kumamoto, Kuan explained that VisEra currently has no plans to follow in TSMC's footsteps. The company's current priority is to reach full capacity and break even at its Longtan plant, to fulfill its previous market commitments. Furthermore, the Longtan plant holds significance for the company due to its higher level of automation compared to its Hsinchu facilities, as higher automation allows for faster production cycles, which in turn benefit both the company's clients and the industry in general.

After the board of directors election (including independent directors) on May 22, Robert Kuan will continue to serve as chairman. In addition to Kuan, new directors include Liu Hsin-Sheng, senior manager of business development at TSMC; and David Liu, director of strategic investments at TSMC. Independent directors include Huang Hui-Chu, Chang Bing-Heng, Chang Mei-Ling, and Lin Han-Fei. The new board will serve from May 22, 2024, to May 21, 2027.

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