M-Space: The Maxeler Collaboration Workspace

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M-Space: The Maxeler Collaboration Workspace is a secure, high-performance computing platform designed to facilitate collaborative research and development. It integrates Maxeler's advanced dataflow computing technology with fragmentiX's multi-cloud storage solutions, ensuring both computational efficiency and robust data protection. Key Features and Functionality: - High-Performance Computing: Utilizes Maxeler's dataflow engines to deliver maximum performance density for complex data processing tasks. - Secure Data Storage: Incorporates fragmentiX's storage architecture, which employs Shamir's Secret Sharing to distribute data fragments across multiple cloud vendors, enhancing data secrecy and resilience. - Scalability: Supports seamless scaling across various public cloud platforms, as well as hybrid and on-premise storage configurations, to meet diverse computational and storage needs. Primary Value and User Solutions: M-Space addresses the critical need for a collaborative environment that combines high computational performance with stringent data security measures. By leveraging FPGA-based processing and advanced data fragmentation techniques, it offers researchers and developers a platform where sensitive data can be processed and stored securely, without compromising on performance. This is particularly beneficial for sectors handling confidential information, such as medical and genomic research, ensuring digital sovereignty and long-term data privacy.

MaxelerOS AMI

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MaxelerOS AMI is a comprehensive operating system designed to optimize the performance of Dataflow Engines within high-performance computing environments. It provides the essential data choreography required to balance resources, maximize utilization, minimize overheads, and manage application acceleration processes at runtime. Key Features and Functionality: - Integrated Hardware and Software: MaxelerOS comprises both hardware components within the FPGA configuration and software running on the main processor, facilitating seamless interaction over interconnects like PCIe, Infiniband, FSB, or HyperTransport. - Runtime DFE Management: The system supports multiple DFEs per node, allowing them to operate independently. It enforces mutual exclusion to ensure that only one application accesses a DFE at any given time. - Efficient Data Transfers: MaxelerOS supports streaming Direct Memory Access for efficient data movement in and out of DFEs, as well as interrupt-based interactions, enhancing data processing efficiency. - Interoperability: Dedicated interfaces are provided to ensure seamless interoperability between hardware blocks, facilitating smooth data transfers and system integration. Primary Value and User Solutions: MaxelerOS AMI addresses the critical need for efficient resource management and application acceleration in high-performance computing. By providing a robust framework for managing DFEs, it enables users to achieve higher computational throughput, reduced latency, and improved overall system performance. This is particularly beneficial for applications requiring intensive data processing and real-time analytics, where maximizing hardware utilization and minimizing overheads are essential.

MaxML CNN

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MaxML CNN is a high-performance computing solution developed by Maxeler Technologies, designed to accelerate the inference of Convolutional Neural Networks (CNNs using Dataflow Engines (DFEs. This toolchain targets multiple FPGAs on a single DFE device, facilitating efficient communication between FPGAs primarily via maxring and, in some cases, utilizing off-chip RAM. By leveraging dataflow computing, MaxML CNN offers a significant performance boost over traditional CPU-based systems, making it ideal for applications requiring rapid and efficient CNN inference. Key Features and Functionality: - Automatic Design Space Exploration (DSE: MaxML CNN automatically searches for optimal loop unrolling parameters using a theoretical performance model, streamlining the design process. - Performance Estimation: The tool provides estimates of design throughput without necessitating full compilation or simulation, enabling efficient performance evaluation. - Compilation Relaxation: In instances where compilation fails, users can relax resource utilization to facilitate design fitting on the FPGA during the MPPR stage, enhancing flexibility. - Simulation Capabilities: MaxML CNN allows for the simulation of maxring connections without reconfiguration by mocking them as PCIe connections to the CPU, which is beneficial for correctness verification. - Custom Precision and Frequency Settings: Users can specify custom fixed-point precisions and FPGA frequencies for each neural network, allowing for tailored performance optimization. Primary Value and Problem Solved: MaxML CNN addresses the challenge of accelerating CNN inference by harnessing the power of DFEs, offering a high-throughput, low-power alternative to conventional GPU-based systems. This solution is particularly advantageous in scenarios where processing speed and energy efficiency are critical, such as real-time image classification and other computationally intensive tasks. By providing a customizable and efficient platform for CNN inference, MaxML CNN enables users to achieve faster results with reduced power consumption, thereby enhancing overall system performance and sustainability.

Maxeler Real Time Risk

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Maxeler Real Time Risk (RTR is a comprehensive suite of financial risk management tools designed to deliver high-performance analytics for complex risk models. Leveraging Maxeler's Dataflow Engines (DFEs, RTR significantly accelerates computations, reducing processing times from hours to minutes and minutes to seconds. This solution is available both on-premise and in the cloud, including compatibility with Amazon EC2 F1 Instances, providing flexibility and scalability to meet diverse organizational needs. Key Features and Functionality: - Comprehensive Risk Tools: RTR includes modules for Credit Value Adjustment (CVA, Standard Initial Margin Model (SIMM, and a full derivatives pricing library, all driven by Bloomberg market data. - High-Performance Computing: Utilizing Maxeler's DFEs, RTR delivers ultra-fast real-time processing capabilities, enabling rapid recalculations of complex risk models. - Flexible Deployment: RTR can be deployed on AWS CPU cloud instances for standard processing or on Amazon EC2 F1 Instances for ultra-fast real-time purposes. - Standardized Data Integration: Clients can upload trades and portfolios using the industry-standard Financial products Markup Language (FpML format, ensuring seamless integration with existing systems. - Customizable Solutions: RTR is ideal for building tailored solutions for Fundamental Review of the Trading Book (FRTB, Counterparty Credit Risk (CCR, and extended scenario analysis, offering complete dashboards and an optional API-based library with full source code. Primary Value and User Benefits: Maxeler Real Time Risk addresses the critical need for rapid and accurate risk assessment in the financial sector. By dramatically reducing computation times, RTR enables financial institutions to perform real-time risk evaluations, enhancing decision-making processes and regulatory compliance. Its flexible deployment options and comprehensive toolset provide organizations with a scalable and efficient solution to manage complex risk models effectively.