FOS: A Modular FPGA Operating System for Dynamic Workloads

With FPGAs now being deployed in the cloud and at the edge, there is a need for scalable design methods which can incorporate the heterogeneity present in the hardware and software components of FPGA systems. Moreover, these FPGA systems need to be maintainable and adaptable to changing workloads while improving accessibility for the application developers. However, current FPGA systems fail to achieve modularity and support for multi-tenancy due to dependencies between system components and lack of standardised abstraction layers. To solve this, we introduce a modular FPGA operating system -- FOS, which adopts a modular FPGA development flow to allow each system component to be changed and be agnostic to the heterogeneity of EDA tool versions, hardware and software layers. Further, to dynamically maximise the utilisation transparently from the users, FOS employs resource-elastic scheduling to arbitrate the FPGA resources in both time and spatial domain for any type of accelerators. Our evaluation on different FPGA boards shows that FOS can provide performance improvements in both single-tenant and multi-tenant environments while substantially reducing the development time and, at the same time, improving flexibility

FPGA Cluster based high performance Cryptanalysis framework

In this paper a ‘FPGA cluster’ based framework for high performance Cryptanalysis has been proposed. The framework abstracts underlying networked FPGA cluster into a unified acceleration resource. It does so by implementing requested amount of computation kernels (cryptographic modules) and managing efficient distribution of the network band-width between the inter-FPGA and intra-FPGA computation kernels. Further agile methodology for developing such networked computation kernels with use of a high level language (Python) based HDL library and seamless integration with a user space crypt analysis application have been discussed. 40-bit partial key attack over AES256 has been demonstrated as a capability demonstration. Performance higher than clustered CPUs and GPUs at lower costs and power is reported