BrainChip Podcast: Insights from Arm's Ian Bratt

29 Jan, 2024

BrainChip Podcast: Insights from Arm's Ian Bratt

Arm's Ian Bratt discusses the evolving landscape of AI and edge computing, emphasizing the growing demand for AI at the edge, the continuous optimization of AI models, and the importance of software and ecosystem development.

In the latest episode of the BrainChip Podcast, Ian Bratt, Senior Director of Technology at Arm, delves into the evolving landscape of AI and edge computing. This discussion is not just a peek into the current state of affairs but a roadmap to the future of AI, especially in the context of edge computing.

AI at the Edge: An Insatiable Demand

The conversation with Ian Bratt highlights a key trend in AI: the shift towards edge computing. Bratt points out the insatiable demand for AI across all levels of technology, emphasizing that significant AI workloads are increasingly being managed at the edge due to their growing demand. This shift is not just a trend but a necessity, driven by the need for more efficient and localized data processing.

The Evolution of AI Models

A fascinating aspect of the discussion revolves around the evolution of AI models. Initially, breakthroughs in AI lead to the creation of large, complex models. However, these models undergo a process of optimization, becoming more compact and efficient for deployment, including at the edge. This cycle of development and optimization is a continuous process in AI, reflecting the dynamic nature of the field.

Challenges in Deploying Edge AI

Deploying AI at the edge comes with its own set of challenges, as Bratt explains. Key among these is the need for efficient data collection and the design of robust neural networks capable of handling diverse scenarios. This part of the discussion is particularly relevant for engineers looking to implement AI solutions in real-world settings.

The Role of Software and Ecosystems

Bratt emphasizes the crucial role of software and ecosystem development in the success of AI. While efficient hardware is fundamental, the software and applications built on top of it are what truly unlock the potential of AI. This segment of the podcast is a call to action for developers and engineers to focus on creating software that maximizes the capabilities of AI hardware.

Standardization: The Key to Edge AI Growth

The podcast touches on an important issue in the field of edge AI: the need for standardization. The current fragmented nature of edge AI poses challenges, and Bratt discusses the push for standardization as a means to help the ecosystem grow. This includes the development of tools and frameworks that simplify the deployment of AI models on edge devices.

Looking Ahead: The Future of Edge AI

Bratt shares his vision for the future of edge AI, foreseeing devices that can run for years on minimal power, possibly through energy harvesting, while still performing sophisticated computing tasks. This forward-looking perspective is particularly exciting for engineers and developers working in the field.

Conclusion: AGI and a Positive Future

In a thought-provoking conclusion, Bratt expresses his optimism about the future of AI, particularly the potential for achieving Artificial General Intelligence (AGI). He envisions a future where AI leads to positive outcomes, countering dystopian narratives.

This episode of the BrainChip Podcast with Ian Bratt is informative and a beacon for those navigating the ever-evolving world of AI and edge computing. For engineers and tech enthusiasts, it's a glimpse into the future, filled with challenges to overcome and exciting possibilities to explore.

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2023 Edge AI Technology Report. Chapter VI: Edge AI Algorithms

What to Consider When Selecting Your Edge AI Hardware and Software.

Wevolver Staff

02 Jul, 2023

artificial intelligence

- computing

- Edge AI

- edge processors

2023 Edge AI Technology Report. Chapter VI: Edge AI Algorithms

Edge AI, empowered by the recent advancements in Artificial Intelligence, is driving significant shifts in today's technology landscape. By enabling computation near the data source, Edge AI enhances responsiveness, boosts security and privacy, promotes scalability, enables distributed computing, and improves cost efficiency.

Wevolver has partnered with industry experts, researchers, and tech providers to create a detailed report on the current state of Edge AI. This document covers its technical aspects, applications, challenges, and future trends. It merges practical and technical insights from industry professionals, helping readers understand and navigate the evolving Edge AI landscape.

Introduction

Artificial Intelligence (AI) deployment on edge devices has become increasingly popular due to its ability to perform tasks locally without relying on cloud services. However, one of the most critical considerations is the selection of a suitable algorithm that is appropriate for the problem it is intended to solve. The complexity, size, and accuracy of AI models can vary significantly, and choosing the best-performing algorithm may not be enough.

In many cases, edge devices have limited computing resources, so the selection of the algorithm should also take into account the computation power of the edge device and whether the chosen algorithm can run smoothly on the hardware. Balancing the algorithm's performance with the available resources is essential to ensure that the edge device can execute the AI model effectively.

While various types of AI algorithms can be successfully utilized on edge devices, including regression and classification algorithms, recent advancements and practical applications have shown that other types of applicable algorithms, such as clustering algorithms and natural language processin

Decoding Transformers on Edge Devices

Bert Moons, Director of System Architecture at AXELERA AI discusses bottlenecks in transformer inference for generative AI.

Bert Moons

fromAxelera AI

11 Jul, 2023

artificial intelligence

- computing

- Edge AI

Recently, transformer-based models have led to significant breakthroughs in several forms of generative AI. They are key in both increasingly powerful text-to-image models, such as DALL-E or stable diffusion, and language and instruction-following models, such as ChatGPT or Stanford’s Alpaca. Today, such networks are typically executed on GPU-based compute infrastructure in the cloud, because of their massive model sizes and high memory and bandwidth requirements. In bringing real-time generative transformers to edge devices, their applicability could be greatly expanded. To this end, this article discusses bottlenecks in transformer inference for generative AI at the edge.

Introducing Transformers

Transformer models, first introduced in the 2017 research appear, ‘Attention Is All You Need’ ^[1] have been firmly established as the state-of-the-art approach in both sequence modeling problems, such as language processing and in image processing ^[2]. Its network architecture is based solely on attention mechanisms, as opposed to Recurrent or Convolutional Neural Networks. Compared to recurrent networks, this makes them much faster to train, as model execution can be parallelized rather than sequentialized. Compared to convolutional neural networks, the attention mechanism increases modelling capacity.

A transformer model typically contains an Encoder and Decoder stack, see Figure 1. Here, the encoder maps an input sequence of tokens, such as words or embedded pixels, onto a sequence of intermediate feature representations. The decoder uses this learned intermediate feature representation to generate an output sequence, one token at a time. The encoder stack exists out of N identical layers, split into multi-head attention-, normalization, elementwise addition, and fully connected feed-forward sublayers. The decoder stack differs in that it inserts a second multi-head cross-attention sublayer, performing attention over the out

BrainChip Podcast: Insights from Arm's Ian Bratt

Arm's Ian Bratt discusses the evolving landscape of AI and edge computing, emphasizing the growing demand for AI at the edge, the continuous optimization of AI models, and the importance of software and ecosystem development.

AI at the Edge: An Insatiable Demand

The Evolution of AI Models

Challenges in Deploying Edge AI

The Role of Software and Ecosystems

Standardization: The Key to Edge AI Growth

Looking Ahead: The Future of Edge AI

Conclusion: AGI and a Positive Future

Search for articles and topics on Wevolver

2023 Edge AI Technology Report. Chapter VI: Edge AI Algorithms

Introduction

Introducing Transformers

Harnessing AI Platforms for Rapid Development and Deployment of Predictive Maintenance Systems in Industrial Applications

Building a Smarter Future: Exploring MCU Integration in Edge AI

Decoding Transformers on Edge Devices