̽��ֱ�� of Cambridge - computer science

Cambridge Festival Speaker Spotlight: Dr Sam Nallaperuma-Herzberg

zs332 — Tue, 11 Mar 2025 13:39:34 +0000

Dr Sam Nallaperuma-Herzberg is a Senior Research Associate at Cambridge ̽��ֱ��’s Department of Computer Science and Technology, and a Fellow in Artificial Intelligence (AI) for Science and Society at the Accelerate Programme for Scientific Discovery.

Arm donates £3.5 million for Cambridge PhD students to study computer architecture and semiconductor design

skbf2 — Wed, 20 Nov 2024 13:05:14 +0000

̽��ֱ��first three students to be supported by the Arm donation will begin their studies at the new research centre in the autumn of 2025. They will be followed by another three students each year for the following four years.

Arm – the company building the future of computing with its global headquarters in Cambridge – is the first organisation to donate to the new CASCADE Research Centre, part of the Department of Computer Science and Technology.

“We’re very grateful to them for their generous support,” said Professor Timothy Jones, Director of the Centre. “As well as funding 15 PhD students over the next five years, Arm’s involvement is helping us realise our vision of a centre where research into addressing key challenges in this field is informed and supported by our industrial partners.This is extremely valuable to us as we work to make the Centre a destination for collaboration between companies, generating pre-competitive open-source artefacts and driving development of novel computer architectures.”

Richard Grisenthwaite, executive vice president and chief architect, Arm said: “Our long-standing commitment to the ̽��ֱ�� of Cambridge through this latest CASCADE funding highlights the vital collaboration between academia and industry as we embark on ground-breaking intent-based programming work to realize the future promise of AI through the next generation of processor designs."

“ ̽��ֱ��Centre has the potential to enable further technology innovation within the semiconductor industry and is an important part of Arm’s mission to build the future of computing.”

Jones added: "Computer architecture is a critical area of computing. It underpins today’s technologies and drives the next generation of computing systems. Here in the Department of Computer Science and Technology, we’re proud of our research and innovation in this area. And the recently published National Semiconductor Strategy underlined how vital such work is, showing that the UK is currently a leader in computer architecture."

"But to maintain this leading position, we need to invest in developing the research leaders of tomorrow. That's why we have established the new CASCADE Research Centre to fund PhD students working in this area, through support from industry. It is currently taking applications for its first cohort of students."

̽��ֱ��Centre will focus on research that addresses some of the grand challenges in computer architecture, design automation and semiconductors.

PhD students will work alongside researchers here who have expertise across the breadth of the area, encompassing the design and optimisation of general-purpose microprocessors, specialised accelerators, on-chip interconnect and memory systems, verification, compilation and networking, quantum architecture and resource estimation. This will allow them to explore the areas they are most passionate about, while addressing industry-relevant research.

Students receiving funding from Arm will be working in the general area of intent-based computing, researching systems that communicate what programs will do in the future so that the processor can make better decisions about how to execute them.

Arm was born in Cambridge in 1990 with the goal of changing the computing landscape. Its success since then in designing, architecting, and licensing high-performance, power-efficient CPUs — the 'brain' of all computers and many household and electronic devices — helped fuel the smartphone revolution and has made it a household name.

Arm has long had a research relationship with Cambridge ̽��ֱ��. Most notably, this has led to the development of new cybersecurity technology, focusing on innovative ways to design the architecture of a computer’s CPU to make software less vulnerable to security breaches.

Adapted from a news release published by the Department of Computer Science and Technology

Arm is donating £3.5 million to enable 15 PhD students over the next five years to study at CASCADE, the ̽��ֱ��'s new Computer Architecture and Semiconductor Design Centre.

Yuichiro Chino via Getty Images

Futuristic circuit board and semiconductor

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Building business partnerships in AI, quantum, cybersecurity and computer architecture

skbf2 — Wed, 18 Sep 2024 14:25:55 +0000

Hear from four of our leading researchers on their work and why partnering with industry is key to their success.

Machine learning gives users ‘superhuman’ ability to open and control tools in virtual reality

sc604 — Wed, 08 Nov 2023 07:44:16 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, used machine learning to develop ‘HotGestures’ – analogous to the hot keys used in many desktop applications.

HotGestures give users the ability to build figures and shapes in virtual reality without ever having to interact with a menu, helping them stay focused on a task without breaking their train of thought.

̽��ֱ��idea of being able to open and control tools in virtual reality has been a movie trope for decades, but the researchers say that this is the first time such a ‘superhuman’ ability has been made possible. ̽��ֱ��results are reported in the journal IEEE Transactions on Visualization and Computer Graphics.

Virtual reality (VR) and related applications have been touted as game-changers for years, but outside of gaming, their promise has not fully materialised. “Users gain some qualities when using VR, but very few people want to use it for an extended period of time,” said Professor Per Ola Kristensson from Cambridge’s Department of Engineering, who led the research. “Beyond the visual fatigue and ergonomic issues, VR isn’t really offering anything you can’t get in the real world.”

Most users of desktop software will be familiar with the concept of hot keys – command shortcuts such as ctrl-c to copy and ctrl-v to paste. While these shortcuts omit the need to open a menu to find the right tool or command, they rely on the user having the correct command memorised.

“We wanted to take the concept of hot keys and turn it into something more meaningful for virtual reality – something that wouldn’t rely on the user having a shortcut in their head already,” said Kristensson, who is also co-Director of the Centre for Human-Inspired Artificial Intelligence.

Instead of hot keys, Kristensson and his colleagues developed ‘HotGestures’, where users perform a gesture with their hand to open and control the tool they need in 3D virtual reality environments.

For example, performing a cutting motion opens the scissor tool, and the spray motion opens the spray can tool. There is no need for the user to open a menu to find the tool they need, or to remember a specific shortcut. Users can seamlessly switch between different tools by performing different gestures during a task, without having to pause their work to browse a menu or to press a button on a controller or keyboard.

“We all communicate using our hands in the real world, so it made sense to extend this form of communication to the virtual world,” said Kristensson.

For the study, the researchers built a neural network gesture recognition system that can recognise gestures by performing predictions on an incoming hand joint data stream. ̽��ֱ��system was built to recognise ten different gestures associated with building 3D models: pen, cube, cylinder, sphere, palette, spray, cut, scale, duplicate and delete.

̽��ֱ��team carried out two small studies where participants used HotGestures, menu commands or a combination. ̽��ֱ��gesture-based technique provided fast and effective shortcuts for tool selection and usage. Participants found HotGestures to be distinctive, fast, and easy to use while also complementing conventional menu-based interaction. ̽��ֱ��researchers designed the system so that there were no false activations – the gesture-based system was able to correctly recognise what was a command and what was normal hand movement. Overall, the gesture-based system was faster than a menu-based system.

“There is no VR system currently available that can do this,” said Kristensson. “If using VR is just like using a keyboard and a mouse, then what’s the point of using it? It needs to give you almost superhuman powers that you can’t get elsewhere.”

̽��ֱ��researchers have made the source code and dataset publicly available so that designers of VR applications can incorporate it into their products.

“We want this to be a standard way of interacting with VR,” said Kristensson. “We’ve had the tired old metaphor of the filing cabinet for decades. We need new ways of interacting with technology, and we think this is a step in that direction. When done right, VR can be like magic.”

̽��ֱ��research was supported in part by the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI).

Reference:
Zhaomou Song; John J Dudley; Per Ola Kristensson. ‘HotGestures: Complementing Command Selection and Use with Delimiter-Free Gesture-Based Shortcuts in Virtual Reality.’ IEEE Transactions on Visualization and Computer Graphics (2023). DOI: 10.1109/TVCG.2023.3320257

Researchers have developed a virtual reality application where a range of 3D modelling tools can be opened and controlled using just the movement of a user’s hand.

We need new ways of interacting with technology, and we think this is a step in that direction

Per Ola Kristensson

HotGestures give users ‘superhuman’ ability to open and control tools in virtual reality

̽��ֱ�� of Cambridge

Modelling a sailboat in virtual reality

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Improved approach to the ‘Travelling Salesperson Problem’ could improve logistics and transport sectors

sc604 — Tue, 26 Apr 2022 09:02:31 +0000

A notorious theoretical question that has puzzled researchers for 90 years, the Travelling Salesperson Problem also has real relevance to industry today. Essentially a question about how best to combine a set of tasks so that they can be performed in the fastest and most efficient way, finding good solutions to the problem can greatly help improve sectors such as transport and logistics.

Researchers from the ̽��ֱ�� of Cambridge have developed a hybrid, data-driven approach to the problem that not only produces high-quality solutions, but at a faster rate than other state-of-the-art approaches. Their results are presented this week at the International Conference on Learning Representations.

“ ̽��ֱ��importance of global logistics system was brought home to us during the pandemic,” said Dr Amanda Prorok from Cambridge’s Department of Computer Science and Technology, who led the research. “We’re highly reliant on this kind of infrastructure to be more efficient – and our solution could help with that as it targets both in-warehouse logistics, such as the routing of robots around a warehouse to collect goods for delivery, and those outside it, such as the routing of goods to people.”

̽��ֱ��Travelling Salesperson Problem involves a notional delivery driver who must call at a set number of cities – say, 20, 50 or 100 – that are connected by highways all in one journey. ̽��ֱ��challenge is to find the shortest possible route that calls at each destination once and to find it quickly.

“There are two key components to the problem. We want to order the stops, and we also want to know the cost, in time or distance, of going from one stop to another in that order,” said Prorok.

Twenty years ago the route from the warehouse to the destinations might have been fixed in advance. But with today’s availability of real-time traffic information, and the ability to send messages to the driver to add or remove delivery locations on the fly, the route may now change during the journey. But minimising its length or duration still remains key.

There’s often a cost attributed to waiting for an optimal solution or hard deadlines at which decisions must be taken. For example, the driver cannot wait for a new solution to be computed – they may miss their deliveries, or the traffic conditions may change again.

And that is why there is a need for general, anytime combinatorial optimisation algorithms that produce high-quality solutions under restricted computation time.

̽��ֱ��Cambridge-developed hybrid approach does this by combining a machine learning model that provides information about what the previous best routes have been, and a ‘metaheuristic’ tool that uses this information to assemble the new route.

“We want to find the good solutions faster,” said Ben Hudson, the paper’s first author. “If I’m a driver for a courier firm I have to decide what my next destination is going to be as I’m driving. I can’t afford to wait for a better solution. So that’s why in our research we focused on the trade-off between the computational time needed and the quality of the solution we got.”

To do this, Hudson came up with a Guided Local Search algorithm that could differentiate routes from one city to another that would be costly – in time or distance – from routes that would be less costly to include in the journey. This enabled the researchers to identify high-quality, rather than optimal, solutions quickly.

They did this by using a measure of what they call the ‘global regret’ – the cost of enforcing one decision relative to the cost of an optimal solution – of each city-to-city route in the Guided Local Search algorithm. They used machine learning to come up with an approximation of this ‘regret’.

“We already know the correct solution to a set of these problems,” said Hudson. “So we used some machine learning techniques to try and learn from those solutions. Based on that, we try to learn for a new problem – for a new set of cities in different locations – which paths between the cities are promising.

“When we have this information, it then feeds into the next part of the algorithm – the part that actually draws the routes. It uses that extra information about what the good paths may be to build a good solution much more quickly than it could have done otherwise.”

̽��ֱ��results they came up with were impressive. Their experiments demonstrated that the hybrid, data-driven approach converges to optimal solutions at a faster rate than three recent learning-based approaches for the Travelling Salesperson Problem.

In particular, when trying to solve the problem when it had a 100-city route, the Cambridge method reduced the mean optimality gap from 1.534% to 0.705%, a two-fold improvement. When generalising from the 20-city problem route to the 100-city problem route, the method reduced the optimality gap from 18.845% to 2.622%, a seven-fold improvement.

“A lot of logistics companies are using routing methods in real life,” said Hudson. “Our goal with this research is to improve such methods so that they produce better solutions – solutions that result in lower distances being travelled and therefore lower carbon emissions and reduced impact on the environment.”

Amanda Prorok is a Fellow of Pembroke College, Cambridge.

Reference:
Benjamin Hudson et al. ‘Graph Neural Network Guided Local Search for the Traveling Salesperson Problem.’ Paper presented at the International Conference on Learning Representations: https://iclr.cc/virtual/2022/calendar.

A new approach to solving the Travelling Salesperson Problem – one of the most difficult questions in computer science – significantly outperforms current approaches.

We’re highly reliant on this kind of infrastructure to be more efficient – and our solution could help with that

Amanda Prorok

Luis Alvarez

Courier checking parcel for delivery

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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̽��ֱ��life of Pi: Ten years of Raspberry Pi

sc604 — Fri, 25 Feb 2022 16:57:58 +0000

̽��ֱ��most successful computer ever to come out of the UK celebrates its tenth anniversary this year.

Cambridge partners with Schmidt Futures in new software engineering network

Anonymous — Wed, 19 Jan 2022 10:39:36 +0000

Schmidt Futures and its partner institutions are establishing the Virtual Institute of Scientific Software (VISS), starting with a network of four centres based at the ̽��ֱ�� of Cambridge, Georgia Institute of Technology, the Johns Hopkins ̽��ֱ�� and the ̽��ֱ�� of Washington.

This interdisciplinary virtual institute will address the growing demand for software engineers with backgrounds in science, complex data and mathematics who can build dynamic, scalable, open software to facilitate accelerated scientific discovery across fields.

While science has become increasingly reliant on complex programming and technology, many researchers lack the training or experience in software engineering, tools and methods to produce effective, reliable, and scalable solutions. As a result, successful research and scientific discovery is sometimes delayed as researchers looking to conduct further experiments struggle to adapt unstable and outdated programming.

VISS seeks to improve the quality of research, accelerate advancements and encourage scalable open-source solutions by providing scientific researchers with access to full-time professional engineers and state of the art technology to develop high quality, maintainable and adaptable software.

“Schmidt Futures’ Virtual Institute for Scientific Software will accelerate the pace of scientific discovery through the development of robust, well-engineered software, supporting longer-term platforms and systems, encouraging best practices in open science, and providing access to techniques such as high-end computing, massive databases, and machine learning,” said Elizabeth McNally, Executive Vice President, Schmidt Futures.

Cambridge's Institute of Computing for Climate Science (ICCS) will apply its existing expertise in climate sciences and artificial intelligence with the research teams from Schmidt Futures’ Virtual Earth Systems Research Institute (VESRI) to address the specific computation and research software needs in the area of climate modelling.

̽��ֱ��centre represents a collaboration between Cambridge Zero, the Departments of Computer Science and Technology, Applied Mathematics and Theoretical Physics, and ̽��ֱ�� Information Services. ̽��ֱ��other three centres will be dedicated to a range of scientific focus areas, including astrophysics, life sciences, engineering and climate.

“With this truly visionary new institute, Cambridge will blend its world-leading climate science, software engineering and computer science expertise,” said Vice-Chancellor Professor Stephen J Toope. “This interdisciplinary powerhouse will enable the development of next-generation climate models. We are delighted to be partnering with Schmidt Futures and engaging with the international research community to inform the response to our most urgent global challenge.”

̽��ֱ��ICCS will be led by Emily Shuckburgh (Academic Director; Cambridge Zero), Dominic Orchard (Co-director; Computer Science/Software Engineering), Chris Edsall (Co-director; Engineering), and Colm-cille Caulfield (Co-director; Science). All have a long-term research agenda to improve understanding of our changing climate through the development, implementation, maintenance, and dissemination of models for scientific computing, data assimilation and analysis.

Being part of the ̽��ֱ��, ICCS will also have a significant education and training component, through the commitment towards sharing its scientific insights openly and broadly. ICCS will play a key role in Cambridge Zero, the ̽��ֱ��'s climate change initiative, that is identifying routes to the creation of a sustainable, zero-carbon future for all.

Over the coming months, ICCS will build a team of researchers and software engineers who share the vision of the power of modern computer science, data science and software engineering for addressing the pressing challenges of our changing climate.

Director of Cambridge Zero and Academic Director of ICCS, Dr Emily Shuckburgh, said “ ̽��ֱ��Institute of Computing for Climate Science will be the first of its kind, supporting the application of the latest developments in computer science and data science to climate modelling. It is tremendously exciting to launch this Institute, which will be the core of an international network of climate research initiatives supported by Schmidt Futures, and will help drive forwards the frontiers of climate science.”

̽��ֱ��interdisciplinary network of centres, which will benefit from the experience of the Schmidt Software Academy at Caltech, will have an initial lifespan of five years.

Adapted from a release published by Schmidt Futures.

Software engineers will bridge the gap between modern science and scalable complex software at four leading universities.

̽��ֱ��Institute of Computing for Climate Science will be the first of its kind, supporting the application of the latest developments in computer science and data science to climate modelling

Emily Shuckburgh

Sir Cam

Centre for Mathematical Sciences

Yes

Can federated learning save the world?

sc604 — Mon, 10 May 2021 11:23:31 +0000

Artificial intelligence models are used increasingly widely in today’s world. Many carry out natural language processing tasks – such as language translation, predictive text and email spam filters. They are also used to empower smart assistants such as Siri and Alexa to ‘talk’ to us, and to operate driverless cars.

But to function well these models have to be trained on large sets of data, a process that includes carrying out many mathematical operations for every piece of data they are fed. And the data sets they are being trained on are getting ever larger: one recent natural language processing model was trained on a data set of 40 billion words.

As a result, the energy consumed by the training process is soaring. Most AI models are trained on specialised hardware in large data centres. According to a recent paper in the journal Science, the total amount of energy consumed by data centres made up about 1% of global energy use over the past decade – equalling roughly 18 million US homes. And in 2019, a group of researchers at the ̽��ֱ�� of Massachusetts estimated that training one large AI model used in natural language processing could generate around the same amount of CO₂ emissions as five cars would generate over their total lifetime.

Concerned by this, researchers in Cambridge's Department of Computer Science and Technology set out to investigate more energy-efficient approaches to training AI models. Working with collaborators at the ̽��ֱ�� of Oxford, ̽��ֱ�� College London, and Avignon Université, they explored the environmental impact of a different form of training – called federated learning – and discovered that it had a significantly greener impact.

Instead of training the models in data centres, federated learning involves training models across a large number of individual machines. ̽��ֱ��researchers found that this can lead to lower carbon emissions than traditional learning.

Senior Lecturer Dr Nic Lane explains how it works when the training is performed not inside large data centres but over thousands of mobile devices – such as smartphones – where the data is usually collected by the phone users themselves.

“An example of an application currently using federated learning is the next-word prediction in mobile phones,” he said. “Each smartphone trains a local model to predict which word the user will type next, based on their previous text messages. Once trained, these local models are then sent to a server. There, they are aggregated into a final model that will then be sent back to all users.”

And this method has important privacy benefits as well as environmental benefits, points out Dr Pedro Porto Buarque De Gusmao, a postdoctoral researcher working with Lane.

"Users might not want to share the content of their texts with a third party,” he said. “In federated learning, we can keep data local and use the collective power of millions of mobile devices together to train AI models without users’ raw data ever leaving the phone.”

“And besides these privacy-related gains,” said Lane, “in our recent research, we have shown that federated learning can also have a positive impact in reducing carbon emissions.

“Although smartphones have much less processing power than the hardware accelerators used in data centres, they don’t require as much cooling power as the accelerators do. That’s the benefit of distributing the training of models across a wide pool of devices.”

̽��ֱ��researchers recently co-authored a paper on this called ‘Can Federated Learning save the planet?’ and will be discussing their findings at an international research conference, the Flower Summit 2021, on 11 May.

In their paper, they offer the first-ever systematic study of the carbon footprint of federated learning. They measured the carbon footprint of a federated learning setup by training two models — one in image classification, the other in speech recognition – using a server and two chipsets popular in the simple devices that targeted by federated methods. They recorded the energy consumption during training, and how it might vary depending on where in the world the chipsets and server were located.

They found that while there was a difference between CO₂ emission factors among countries, federated learning under many common application settings was reliably ‘cleaner’ than centralised training.

Training a model to classify images in a large image dataset, they found any federated learning setup in France emitted less CO₂than any centralised setup in both China and the US. And in training the speech recognition model, federated learning was more efficient than centralised training in any country.

Such results are further supported by an expanded set of experiments in a follow-up study (‘A first look into the carbon footprint of federated learning’) by the same lab that explores an even wider variety of data sets and AI models. And this research also provides the beginnings of necessary formalism and algorithmic foundation of even lower carbon emissions for federated learning in the future.

Based on their research, the researchers have made available a first-of-its-kind ‘Federated Learning Carbon Calculator’ so that the public and other researchers can estimate how much CO₂is produced by any given pool of devices. It allows users to detail the number and type of devices they are using, which country they are in, which datasets and upload/download speeds they are using and the number of times each device will train on its own data before sending its model for aggregation.

They also offer a similar calculator for estimating the carbon emissions of centralised machine learning.

“ ̽��ֱ��development and usage of AI is playing an increasing role in the tragedy that is climate change,” said Lane, “and this problem will only worsen as this technology continues to proliferate through society. We urgently need to address this which is why we are keen to share our findings showing that federated learning methods can produce less CO₂ than data centres under important application scenarios.

“But even more importantly, our research also shines a light as to how federated learning should evolve towards being even more broadly environmentally friendly. Decentralized methods like this will be key in the invention of future sustainable forms of AI in the years ahead.”

̽��ֱ��researchers will be presenting their work at the Flower Summit 2021, which takes place on Tuesday 11 May 2021.
Can Federated Learning Save the Planet, Xinchi Qiu, Titouan Parcollet, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane.
Federated Learning Carbon Calculator
A first look into the carbon footprint of federated learning, Xinchi Qiu, Titouan Parcollet, Javier Fernandez-Marques, Pedro Porto Buarque de Gusmao, Daniel J. Beutel, Taner Topal, Akhil Mathur, Nicholas D. Lane

Training the artificial intelligence models that underpin web search engines, power smart assistants and enable driverless cars, consumes megawatts of energy and generates worrying carbon dioxide emissions. But new ways of training these models are proven to be greener.

̽��ֱ��development and usage of AI is playing an increasing role in the tragedy that is climate change, and this problem will only worsen as this technology continues to proliferate through society

Nic Lane

Image via www.vpnsrus.com

Machine Learning & Artificial Intelligence

Yes

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