Quick Recap: We introduce a Basic v2 program specially crafted for the iconic Commodore 64 computer, harnessing the power of a simple neural network with leaky integrate-and-fire neurons and volatile memristor simulation for scanline rendering.Our neuromorphic scanline rendering program demonstrates the endless possibilities where every scanline becomes a stroke of artistic brilliance.

Neuromorphic Computing Unleashed: Neuromorphic computing is a revolutionary approach that draws inspiration from the human brain’s neural networks to solve complex computational problems. In our quest to reimagine scanline rendering, we leverage the Commodore 64’s capabilities, creating a virtual neural network for artistic outputs. At the heart of our program lies a neural network composed of leaky integrate-and-fire neurons. These neurons mimic the way brain cells function, receiving inputs from neighboring neurons and generating outputs based on adjustable memristor values.

The key to our neuromorphic approach lies in simulating volatile memristors, which behave similarly to synapses in the brain. These memristors store weights that determine the strength of connections between neurons. By updating these memristors during rendering, the neural network adapts and refines its artistic representations.

The Rendering Process: As the program processes each scanline of the image, it evaluates the memristor values and performs calculations using the leaky integrate-and-fire model. The stochastic nature of the model allows for artistic variation, making each rendering unique. Our project combines artistry and technology, transforming a vintage computer into a canvas for generative artwork. The dynamic neural network, paired with the retro aesthetic of the Commodore 64, produces stunning and unpredictable visual outputs that push the boundaries of creativity.

UPDATE: Please download and use neuralscanrndr2 file, it relieves the cpu of nonessential work for faster generation! Thank you.

Links(rename in .bas for the compiler)

L'articolo Neuromorphic Scanline Rendering on Commodore 64 for Visual Image Generation(updated!) proviene da GXVTRONICS.

Quick Recap: Quantization is a technique used in machine learning to reduce the precision of weight values in a model, which can result in significant reductions in memory usage and computations required.
Program Description:
The program, written in BASIC v2, starts by defining a neural network with three layers and ten weights per layer. It then trains the model using a random dataset, demonstrating how to implement the forward pass and backward pass algorithms. After training, the program shows how to quantize the model weights using a mean-based quantization method. This method reduces the number of bits used to represent the weights while preserving the model’s accuracy. Finally, the program demonstrates how to use the quantized weights for inference or further optimization.

Features and Functionality:

• Implementation of a simple neural network with three layers and ten weights per layer
• Train the model using a random dataset
• Demonstration of forward pass and backward pass algorithms
• Quantization of model weights using a mean-based quantization method
• Preservation of model accuracy after quantization
• Use of quantized weights for inference or further optimization

Target Audience:
This program targeted for the c64 platform is suitable for those interested in exploring quantization techniques in machine learning, particularly for educational purposes. It provides a basic understanding of how quantization works and how it can be applied to a simple neural network. However, it’s important to note that the program doesn’t include any actual data or real-world examples, so its practical applications may be limited.

Links(source included in .txt, please rename in .bas for the compiler)

L'articolo Exploring Quantization Techniques in Machine Learning: A Simple Demo Program for C64 proviene da GXVTRONICS.

Quick recap
Overall, reservoir computing is an effective tool for simplifying RNNs and improving their performance. It addresses issues such as vanishing gradients and exploding activations, while also allowing for faster training times and the ability to handle large datasets. Additionally, reservoir computing integrated with photonic devices opens up possibilities for high-speed neuromorphic computing applications, such as image recognition and brain-inspired computing. With its advantages in efficiency and accuracy, reservoir computing presents a promising avenue for further exploration and development in various fields.

Introduction:
Recurrent Neural Networks (RNNs) are widely used in various applications such as natural language processing, speech recognition, and time series forecasting. However, training RNNs can be computationally expensive and challenging, especially when dealing with large datasets. To address these limitations, researchers have proposed several techniques, including reservoir computing, which simplifies RNNs by leveraging photonic devices. In this blog post, we’ll explore how reservoir computing can simplify RNNs and improve their performance.
What is Reservoir Computing?
Reservoir computing is a machine learning approach that utilizes a nonlinear dynamic system, called a reservoir, to perform complex computations. The reservoir consists of a set of interconnected nodes that are driven by external inputs. By adjusting the input signals, the reservoir’s states can be controlled, allowing it to perform various tasks such as time series prediction, classification, and optimization.
How Does Reservoir Computing Simplify RNNs?
Traditional RNNs rely on recurrence to capture temporal dependencies in data, but this also makes them prone to vanishing gradients and exploding activations. Reservoir computing alleviates these issues by using a fixed, random set of weights to connect the input nodes to the reservoir’s hidden layers. This eliminates the need for backpropagation through time, reducing computational complexity and enabling faster training times.
Another advantage of reservoir computing is its ability to handle large datasets without sacrificing performance. Unlike traditional RNNs, reservoir computing can process multiple inputs simultaneously, making it well-suited for parallel computing architectures like graphics processing units (GPUs).

Applications of Reservoir Computing in Neuromorphic Computing:
Photonic reservoir computing has gained significant attention in recent years due to its potential for high-speed neuromorphic computing applications. By integrating photonic devices into reservoir computing systems, researchers can achieve faster processing speeds and lower power consumption compared to traditional electronic implementations.
One promising application of photonic reservoir computing is in image recognition. Researchers have demonstrated that photonic reservoir computers can classify images at rates exceeding 100 Gbps, significantly faster than existing electronic systems. Another potential application is in brain-inspired computing, where reservoir computing can simulate the behavior of neural networks more accurately and efficiently than traditional digital approaches.
Conclusion:
In conclusion, reservoir computing offers a powerful alternative to traditional RNNs by simplifying their architecture and improving their performance. By leveraging photonic devices, reservoir computing can enable high-speed neuromorphic computing applications that are both efficient and accurate.

Video(various sources)

Links
https://scholar.google.be/citations?view_op=view_citation&hl=en&user=9FVK1LIAAAAJ&citation_for_view=9FVK1LIAAAAJ:YsMSGLbcyi4C
https://www.researchgate.net/publication/254055707_Photonic_reservoir_computing_and_information_processing_with_coupled_semiconductor_optical_amplifiers

L'articolo Simplifying Recurrent Neural Networks (RNNs) with Reservoir Computing and Photonic: A Machine Learning Approach proviene da GXVTRONICS.

Cathie Wood, the CEO and founder of ARK Investment Management, recently shared that her firm has shifted away from Nvidia as a prominent artificial intelligence (AI) investment. Instead, they have reallocated their investments into other companies involved in AI. Wood closed out her firm’s position in Nvidia in January, when she believed that the chipmaker had reached its full potential. However, she now recognizes that Nvidia has continued to perform exceptionally well, but there are other companies with significant growth potential in the AI sector. As a result, Wood has started purchasing shares of these companies, which she believes can benefit greatly from the transformative power of AI technology. (Possibly AMD?Intel?)

Links
https://www.bloomberg.com/news/articles/2023-07-17/cathie-wood-says-ark-has-moved-beyond-nvidia-as-obvious-ai-buy-lk7eatb4

L'articolo Cathie Wood’s ARK Investment Management Shifts Focus in AI Investments, Moves Away from Nvidia proviene da GXVTRONICS.

Hopfield network memory: imagine you have a Hopfield network, a computer network inspired by our brain’s memory storage and recall mechanisms. It consists of interconnected nodes, or artificial neurons, that can be either ON or OFF. To store memories, each neuron’s state is updated based on the states of all other neurons, aiming for a stable pattern representing the memory. When recalling a memory, you input a partial or corrupted version of its pattern, and the network iteratively updates its neurons until it converges to the closest matching pattern. In summary, a modern Hopfield network allows you to store memories as patterns of neuron states and retrieve them by providing related patterns. It serves as a content-addressable memory system. And improved designs of modern Hopfield networks offer higher memory capacity, better noise tolerance, and more efficient recall mechanisms.

In Dmitry Krotov, PhD’s video on the large associative memory problem in neurobiology and machine learning, various topics are covered to understand the challenges and potential solutions for creating large-scale associative memory systems.

Video

The video explores associative memory’s importance in neurobiology and machine learning, as it enables us to remember information and its relationships. It examines the challenges in creating large-scale associative memory systems, including the limitations of existing models and the relationship between neural correlations and associative memory in the human brain.

Sparse coding, an efficient information representation, is introduced as a means to enhance associative memory systems. However, Hopfield networks, a neural network model for associative memory, have capacity and storage limitations. Alternative approaches, such as employing more complex architectures, attention mechanisms, and unsupervised learning techniques, are proposed to tackle the large associative memory problem.

The video also discusses the future of associative memory research, emphasizing interdisciplinary collaboration for advancements in both neurobiology and machine learning. By addressing the large associative memory problem, the video aims to enhance our understanding of potential solutions and challenges, ultimately contributing to the development of more intelligent and efficient artificial intelligence systems that closely resemble human cognitive abilities.

Links
https://research.ibm.com/publications/modern-hopfield-networks-in-ai-and-neurobiology

L'articolo What about a memory for AI? Improving Hopfield Memories. Enhancing Storage Capacity, Noise Tolerance, and Recall Efficiency proviene da GXVTRONICS.

Scalability is a crucial aspect of neuromorphic computing systems, and there are various approaches to achieving it. Optical interconnects and specialized hardware like Field-Programmable Gate Arrays (FPGAs) or Application-Specific Integrated Circuits (ASICs) contribute significantly to the scalability of these systems.

However, before widespread adoption of neuromorphic computing can occur, several challenges need to be addressed. These challenges include improving hardware efficiency, developing advanced algorithms, and integrating neuromorphic computing with traditional computing approaches.

One specific type of neuromorphic hardware called Spiking Neural Networks (SNNs) mimics the behavior of biological nervous systems and offers several advantages. These advantages include low power consumption, real-time processing capabilities, and adaptive learning. Consequently, SNNs are particularly suitable for energy-efficient and adaptable applications.

The Dynamic Vision Sensor (DVS) is a specialized vision sensor widely used in neuromorphic computing. It operates on an event-based principle and consumes minimal power. The DVS finds applications in object detection and recognition in computer vision, robotics, drones, and smart cameras.

Brain-inspired algorithms, such as Spike-Timing-Dependent Plasticity (STDP) and Adaptive Hebbian Learning (AHL), play a crucial role in enhancing the performance and adaptability of SNNs. Implementing these algorithms in SNNs significantly improves their functionality and efficiency.

Efficient hardware-software co-design is essential for achieving optimal performance and energy efficiency in SNN systems. By developing methods that optimize the collaboration between hardware and software components, it becomes possible to integrate and utilize resources effectively, ultimately improving system performance.

The training and optimization of SNNs pose unique challenges due to their size and the need for parallel processing. However, recent advancements in both hardware and software have made these processes more feasible. Ongoing research focuses on further enhancing these processes to overcome existing obstacles.

With their energy efficiency and adaptability, SNNs have the potential to revolutionize computing systems, making them more efficient, scalable, and capable of handling complex tasks accurately. As research and development progress in neuromorphic computing, we can expect to see even more innovative applications and breakthroughs that will shape the future of technology. This progress will enable us to tackle significant challenges in areas such as artificial intelligence, robotics, and energy efficiency.

Video

Links

https://www.intel.com/content/www/us/en/research/neuromorphic-community.html

L'articolo Scalability and Advancements in Neuromorphic Computing: Revolutionizing Technology with Spiking Neural Networks proviene da GXVTRONICS.

The artificial intelligence landscape is constantly evolving, and with the emergence of new AI processors, the possibilities for innovation are endless. AMD, a leading technology company, has been making waves in the AI industry with their groundbreaking AMD Mi250 chip. Recently, MosaicML, a software company specializing in AI infrastructure, has demonstrated that their stack fully supports the AMD Mi250. This development has the potential to revolutionize the AI market, offering more efficient and cost-effective solutions for businesses and organizations.

AMD Mi250: A Game-Changer in AI Processing

The AMD Mi250 is a powerful AI processor designed to deliver exceptional performance at a lower cost compared to other AI accelerators in the market.

Specifications:

The AMD Instinct MI250 is a GPU designed for server platforms. It features the CDNA2 architecture and is built on a 6nm FinFET process by TSMC. The GPU has 13,312 stream processors and 208 compute units.

In terms of performance, it can deliver a peak half precision (FP16) performance of 362.1 TFLOPs and a peak single precision (FP32) performance of 90.5 TFLOPs. It also has a peak double precision (FP64) performance of 90.5 TFLOPs. Additionally, it has peak INT4 and INT8 performance of 362.1 TOPs, and peak bfloat16 performance of 362.1 TFLOPs.

The GPU comes with 128 GB of dedicated HBM2e memory with a memory clock of 1.6 GHz and a memory interface of 8192-bit. It supports memory ECC and has a peak memory bandwidth of up to 3276.8 GB/s.

The board has a total board power (TBP) of 500W (560W peak) and uses a PCIe 4.0 x16 bus. It features passive OAM cooling and has 8 Infinity Fabric links with a peak bandwidth of 100 GB/s.

In terms of software, it supports Linux x86_64 operating system and various software technologies such as AMD CDNA 2 Architecture, AMD Infinity Architecture, AMD ROCm, and RAS Support. It also supports OpenMP, OpenCL, HIP, and various frameworks like TensorFlow, PyTorch, Kokkos, and RAJA.

The product was launched on November 8, 2021, as part of the AMD Instinct MI Series.

MosaicML: A Comprehensive AI Infrastructure Solution

MosaicML is a software company that provides a complete stack for building, deploying, and managing AI models. Their platform enables developers to create, train, and optimize models more efficiently, while also offering seamless integration with popular cloud services and on-premises infrastructure.

The Demonstration: A New Era of AI Processing

Recently, MosaicML has demonstrated that their stack fully supports the AMD Mi250. This development signifies a significant milestone in the AI industry, as it opens up new possibilities for businesses and organizations to harness the power of the AMD Mi250 without any compatibility issues.

With the AMD Mi250 now fully supported by MosaicML, users can expect to experience the following benefits:

1. Enhanced performance: The AMD Mi250’s powerful GPU cores enable faster training and inference of AI models, leading to improved overall performance and reduced processing times.
2. Cost-effective AI solutions: The AMD Mi250’s competitive pricing makes it an attractive option for businesses looking to implement AI solutions without breaking the bank.
3. Scalability: The AMD Mi250’s versatile architecture allows for easy scaling of AI infrastructure, making it an ideal choice for businesses experiencing rapid growth or those with fluctuating workloads.
4. Ease of integration: MosaicML’s support for the AMD Mi250 ensures seamless integration with existing AI infrastructure, enabling businesses to leverage their existing investments while benefiting from the advanced capabilities of the AMD Mi250.

The demonstration of MosaicML’s full support for the AMD Mi250 marks a significant milestone in the AI industry. This collaboration promises to bring more efficient and cost-effective AI processing solutions to businesses and organizations, paving the way for a new era of innovation and growth. As AI continues to transform the way we live and work, the AMD Mi250 and MosaicML’s support will undoubtedly play a crucial role in shaping the future of AI processing.

Links
https://twitter.com/NaveenGRao/status/1674815632523640836
https://www.reuters.com/technology/amds-ai-chips-could-match-nvidias-offerings-software-firm-says-2023-06-30/

L'articolo MosaicML Demonstrates Full Support for AMD Mi250(FULL specs here): A New Era of AI Processing Begins proviene da GXVTRONICS.

The Von Neumann architecture, which underlies modern x86 computing, has been the cornerstone of computing for over half a century. However, this architecture has limitations that have become increasingly apparent as we strive to create more powerful and efficient computing systems. In this blog post, we will explore these limitations and compare them to the brain-inspired alternative, neuromorphic computing.

Limitations of Von Neumann Architecture

1. Data Dependency: In Von Neumann architecture, the CPU depends on data stored in memory, which leads to data transfer bottlenecks. This data dependency results in reduced efficiency and increased energy consumption.
2. Sequential Processing: Von Neumann architecture follows a sequential processing model, which limits the ability to perform multiple tasks simultaneously. This limits the potential for parallel computing and makes it difficult to achieve true concurrency.
3. Memory Bandwidth Constraints: The Von Neumann architecture’s shared bus structure has limited memory bandwidth, which hinders the transfer of data between the CPU and memory. This bottleneck can significantly impact the overall performance of the system.
4. Heat Generation: As computing power increases, so does the generation of heat. This heat can cause significant performance degradation and even system failure.

Neuromorphic Computing: The Brain-Inspired Alternative

1. Energy Efficiency: Neuromorphic computing, inspired by the human brain, can perform tasks with much lower energy consumption than traditional computing. This is because the brain is highly efficient at performing tasks with minimal energy expenditure.
2. Parallel Processing: Neuromorphic architectures enable parallel processing, allowing multiple tasks to be performed simultaneously. This is similar to how the human brain processes information in a distributed manner.
3. Scalability: Neuromorphic architectures can be scaled to accommodate a wide range of tasks and applications, making them highly versatile.
4. Adaptability: Neuromorphic computing systems can adapt to new tasks and environments without the need for extensive reprogramming. This adaptability is akin to the brain’s ability to learn and change.

As computing demands continue to grow, the limitations of the Von Neumann architecture become increasingly apparent. Neuromorphic computing represents a promising alternative that can address these limitations and offer significant advantages. By mimicking the brain’s distributed and parallel processing model, neuromorphic computing systems have the potential to revolutionize the field of computing.

However, it is important to note that neuromorphic computing is still in its early stages of development, and there are several challenges that need to be overcome, such as the integration of large-scale systems and the development of efficient hardware and software platforms.

Despite these challenges, the potential benefits of neuromorphic computing make it a highly attractive alternative to traditional computing. As researchers continue to explore the brain-inspired approach to computing, it is likely that we will see significant advancements in the field, leading to more efficient, powerful, and adaptable computing systems.

In summary, the limitations of the Von Neumann architecture make it clear that we need a new paradigm for computing. Neuromorphic computing, with its brain-inspired approach, offers a promising alternative that can potentially revolutionize the field of computing. By overcoming the limitations of traditional computing, neuromorphic systems have the potential to enable a new era of computing that is more efficient, adaptable, and powerful than ever before.

Video

Links
https://learn.microsoft.com/en-us/windows-hardware/drivers/debugger/x86-architecture
https://www.intel.com/content/www/us/en/research/neuromorphic-computing.html

L'articolo Unveiling the Boundaries: A Closer Look at Von Neumann Architecture and Neuromorphic Computing as Brain-Inspired Alternatives proviene da GXVTRONICS.

Very large models can be quite difficult to train due to memory constraints. With Megatron-LM, they can achieve training very large transformer models and implement a simple, efficient intra-layer model parallel approach that enables training transformer models with billions of parameters.So this approach does not require a new compiler or library changes.

Links
https://arxiv.org/abs/1909.08053

L'articolo Megatron-LM scales up neural networks proviene da GXVTRONICS.