BrainChip's Deep Dive into AI's Future: A Podcast with Tenstorrent's COO Keith Witek

Jessica Miley

29 Dec, 2023

BrainChip's Deep Dive into AI's Future: A Podcast with Tenstorrent's COO Keith Witek

Navigating AI's Future in a Riveting Podcast Discussion

artificial intelligence

- Edge AI

- machine learning

- neuromorphic computing

For those eager to grasp the intricacies of AI and neuromorphic computing BrainChip's podcast series stands out as a strong source of knowledge. In their 28th episode titled "This Is Our Mission," BrainChip's Rob Telson orchestrates a riveting conversation with Tenstorrent's COO, Keith Witek, and BrainChip's CMO, Nandan Nayampally. This episode provides a treasure trove of insights, making it a must-listen for engineers working in the industry or warning to know more about the cutting edge of computing.

About the Guests

Rob Telson, the Vice President of Ecosystem and Partnerships at BrainChip, is the dynamic force behind the podcast series. With a keen eye for curating meaningful discussions, Telson ensures that each episode is both enlightening and engaging. His role at BrainChip involves fostering partnerships and building a robust ecosystem, making him the perfect host to bridge the gap between technical intricacies and their real-world implications.

Keith Witek, the COO of Tenstorrent, boasts an impressive resume. With degrees spanning Business, Computing, Electrical Engineering, and Law, Witek has left his mark at tech giants like Google and Tesla. His expertise in strategic alliances, especially in consumer electronics, sets the stage for a profound discussion.

Nandan Nayampally, BrainChip's CMO, brings to the table a rich history in the semiconductor domain. With stints at AMD, Denali Software, and even Amazon's Alexa, Nayampally's insights into computing trends are invaluable.

Podcast Key Discussion Points

AI's Staying Power: The realm of technology is no stranger to fleeting trends, but AI is not one of them. As discussed in the podcast, AI has firmly established itself as a transformative force, with its applications and implications reaching far and wide. The consensus between the experts is clear: AI's influence in shaping the future is undeniable, and its role in various sectors is only set to grow.

Real-world AI Impacts: The podcast delves into tangible ways AI is making a difference in our lives. From the significant reduction in car-related fatalities due to AI-driven safety features to the potential of personalized healthcare solutions tailored to individual needs, AI's footprint in real-world scenarios is expanding rapidly.

Challenges in AI Scalability: Despite its vast potential, AI faces its set of challenges. The podcast highlights issues like the massive influx of data that systems need to process and the impending slowdown of Moore’s Law. These challenges pose questions about the scalability and efficiency of current AI models.

Innovative Solutions: The conversation takes an optimistic turn when discussing the solutions. Industry leaders, including BrainChip and Tenstorrent, are at the forefront of addressing these challenges. Their innovative approaches and technologies promise to overcome existing barriers, paving the way for a more AI-integrated future.

Promising Tech Deployment Models: The podcast also touches upon the future of technology deployment. The concept of chiplets, for instance, offers a glimpse into the potential shifts in how we might approach hardware design and integration in the coming years, ensuring more efficient and scalable AI implementations.

Closing Thoughts

This podcast episode is a great resource for anyone interested in the future of AI, regardless of their background or level of knowledge. It promises to offer insights into real-world applications, challenges, and solutions that are shaping the evolution of AI. As the world stands at the brink of an AI revolution, it becomes increasingly important to understand its nuances and potential implications.

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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's Deep Dive into AI's Future: A Podcast with Tenstorrent's COO Keith Witek

Navigating AI's Future in a Riveting Podcast Discussion

About the Guests

Podcast Key Discussion Points

Closing Thoughts

Search for articles and topics on Wevolver

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

Introduction

Introducing Transformers

2023 Edge AI Technology Report. Chapter IV: Hardware & Software Selection

Bringing Intelligence to the Edge Series

Decoding Transformers on Edge Devices