Current activity with the EVE ZeBu Hardware/Software Co-Verification Environment

The title is a fancy way of referring to the EVE ZeBu accelerators (much more information on their website). I previously posted on our acquisition of a UF-2 (I should mention we liked it so much we now have 2!) so I am taking a moment to show what we are up to with this wonderful technology. I have broken it into research and teaching topics.

Research
Behavioural Simulation and Synthesis of Biological Neuron Systems using VHDL
The investigation of neuron structures is an incredibly difficult and complex task that yields relatively low rewards in terms of information from biological forms (either animals or tissue). The structures and connectivity of even the simplest invertebrates are almost impossible to establish with standard laboratory techniques, and even when this is possible it is generally time consuming, complex and expensive. Recent work has shown how a simplified behavioural approach to modelling neurons can allow “virtual” experiments to be carried out that map the behaviour of a simulated structure onto a hypothetical biological one, with correlation of behaviour rather than underlying connectivity. The problems with such approaches are numerous. The first is the difficulty of simulating realistic aggregates efficiently, the second is making sense of the results and finally, it would be helpful to have an implementation that could be synthesised to hardware for acceleration. In this paper we present a VHDL implementation of Neuron models that allow large aggregates to be simulated. The models are demonstrated using a synthesizable system level VHDL model of the C. Elegans locomotory system.
The role of the EVE in this specific project is verifying and executing the largest of the neural net models functionality using your cosimulation replacing previous, limited, technology based around a FPGA board using a probe program.
Publications
Bailey, J., Wilson, P., Brown, A. and Chad, J. (2008) Behavioural Simulation and Synthesis of Biological Neuron Systems using VHDL. In: BMAS. (In Press)
Bailey, J., Wilson, P. R., Brown, A. D. and Chad, J. (2007) Behavioural Simulation of Biological Neuron Systems using VHDL and VHDL-AMS. In: IEEE Behavioural Modeling and Simulation, Sep 2007, San Jose, USA. pp. 153-158.

Architectures for Numerical Computation

Since the 1960s, the observation that has become known as Moore’s Law has become a self-fulfilling prophecy. Processing power doubles every two years because of the advances in CMOS technology. There are clear signs, however, that these technological advances are coming to an end. The eco- nomics of pushing CMOS technology to its physical limits will eventually halt further development.
If it is no longer feasible to increase computing power through smaller, faster transistors, the al- ternative is massive parallelism. This progression is already apparent. Multi-core and multi-threaded processors are now common. Although modern operating systems are able to use multiple cores, with few exceptions, programs are confined to single cores. The challenge facing software engineers is to make best use of multiple cores.
A significant amount of processing power is concerned with numerical computation. Consumer applications, such as image and audio processing are fundamentally numerical. Similarly, engineering applications, such as simulation and optimization rely on numerical calculations. At this point, we should distinguish between consumer and desktop applications and High-Performance Computing (HPC) tasks that rely on clusters of dedicated processors. It is not our intention to move into the HPC world at this time.
While using multiple cores can accelerate many numerical algorithms, far greater speed-up would be possible using more specialized forms of hardware, such as GPUs and FPGAs. A further consideration is power consumption (and the related problem of heat dissipation). Custom hardware can reduce power consumption by an order of magnitude or more. The key, of course, is to use the resources in the best possible way. In the context of the work proposed here, there are two aspects to this problem. First, we need to make the best division between hardware and software and second, we need to design an appropriate overall architecture.
The obvious role of the EVE platform in this research field is to support the research into specific computing pipelines, fine grain computation blocks and architectures as well as enabling the development of some 16 lane PCIe computation accelerators.

Teaching
Hosting a Complex SoC on the EVE Platform
This project was a proof of concept and de-risking of the EVE transactor flow using a large SoC. The SoC in question was chosen to be the Gaisler-Aeroflex LEON3 (http://www.gaisler.com). The LEON3 SoC is based around a SPARC v8 compatible CPU and is written in VHDL. The minimal LEON3 SoC was built using the EVE support for memory to model the processor cache along with the transactors for DRAM and UARTs. This work will be extended to include the VGA, Ethernet and USB transactors on the hardware side it and include support for the Snapgear Linux version for the LEON3.

Verification of a highly integrated ASIC
A large masters level project framework will produce a heterogeneous multicore ASIC to perform processing on HD video data streams. It will bundle an 32 bit microcontroller core, on chip SRAM, our custom geometric processor along with a multilayer AMBA bus architecture optimised for power and contention. The EVE will be an invaluable support to the simulation and verification of the final design before it is sent for manufacture.

So there you have it - quite a lot going on, all of which is really fascinating and fun!