Vector Microprocessor Research Articles

Memory Controller for Vector Processor

To manage power and memory wall affects, the HPC industry supports FPGA reconfigurable accelerators and vector processing cores for data-intensive scientific applications. FPGA based vector accelerators are used to increase the performance of high-performance application kernels. Adding more vector lanes does not affect the performance, if the processor/memory performance gap dominates. In addition if on/off-chip communication time becomes more critical than computation time, causes performance degradation. The system generates multiple delays due to application’s irregular data arrangement and complex scheduling scheme. Therefore, just like generic scalar processors, all sets of vector machine – vector supercomputers to vector microprocessors – are required to have data management and access units that improve the on/off-chip bandwidth and hide main memory latency. In this work, we propose an Advanced Programmable Vector Memory Controller (PVMC), which boosts noncontiguous vector data accesses by integrating descriptors of memory patterns, a specialized on-chip memory, a memory manager in hardware, and multiple DRAM controllers. We implemented and validated the proposed system on an Altera DE4 FPGA board. The PVMC is also integrated with ARM Cortex-A9 processor on Xilinx Zynq All-Programmable System on Chip architecture. We compare the performance of a system with vector and scalar processors without PVMC. When compared with a baseline vector system, the results show that the PVMC system transfers data sets up to 1.40x to 2.12x faster, achieves between 2.01x to 4.53x of speedup for 10 applications and consumes 2.56 to 4.04 times less energy.

Future vector microprocessor extensions for data aggregations

As the rate of annual data generation grows exponentially, there is a demand to aggregate and summarise vast amounts of information quickly. In the past, frequency scaling was relied upon to push application throughput. Today, Dennard scaling has ceased and further performance must come from exploiting parallelism. Single instruction-multiple data (SIMD) instruction sets offer a highly efficient and scalable way of exploiting data-level parallelism (DLP). While microprocessors originally offered very simple SIMD support targeted at multimedia applications, these extensions have been growing both in width and functionality. Observing this trend, we use a simulation framework to model future SIMD support and then propose and evaluate five different ways of vectorising data aggregation. We find that although data aggregation is abundant in DLP, it is often too irregular to be expressed efficiently using typical SIMD instructions. Based on this observation, we propose a set of novel algorithms and SIMD instructions to better capture this irregular DLP. Furthermore, we discover that the best algorithm is highly dependent on the characteristics of the input. Our proposed solution can dynamically choose the optimal algorithm in the majority of cases and achieves speedups between 2.7 × and 7.6 × over a scalar baseline.

A RISC-V Vector Processor With Simultaneous-Switching Switched-Capacitor DC–DC Converters in 28 nm FDSOI

This work demonstrates a RISC-V vector microprocessor implemented in 28 nm FDSOI with fully integrated simultaneous-switching switched-capacitor DC–DC (SC DC–DC) converters and adaptive clocking that generates four on-chip voltages between 0.45 and 1 V using only 1.0 V core and 1.8 V IO voltage inputs. The converters achieve high efficiency at the system level by switching simultaneously to avoid charge-sharing losses and by using an adaptive clock to maximize performance for the resulting voltage ripple. Details about the implementation of the DC–DC switches, DC–DC controller, and adaptive clock are provided, and the sources of conversion loss are analyzed based on measured results. This system pushes the capabilities of dynamic voltage scaling by enabling fast transitions (20 ns), simple packaging (no off-chip passives), low area overhead (16%), high conversion efficiency (80%–86%), and high energy efficiency (26.2 DP GFLOPS/W) for mobile devices.

An Agile Approach to Building RISC-V Microprocessors

The final phase of CMOS technology scaling provides continued increases in already vast transistor counts, but only minimal improvements in energy efficiency, thus requiring innovation in circuits and architectures. However, even huge teams are struggling to complete large, complex designs on schedule using traditional rigid development flows. This article presents an agile hardware development methodology, which the authors adopted for 11 RISC-V microprocessor tape-outs on modern 28-nm and 45-nm CMOS processes in the past five years. The authors discuss how this approach enabled small teams to build energy-efficient, cost-effective, and industry-competitive high-performance microprocessors in a matter of months. Their agile methodology relies on rapid iterative improvement of fabricatable prototypes using hardware generators written in Chisel, a new hardware description language embedded in a modern programming language. The parameterized generators construct highly customized systems based on the free, open, and extensible RISC-V platform. The authors present a case study of one such prototype featuring a RISC-V vector microprocessor integrated with a switched-capacitor DC-DC converter alongside an adaptive clock generator in a 28-nm, fully depleted silicon-on-insulator process.

Conflict-Free Accesses to Strided Vectors on a Banked Cache

With the advance of integration technology, it has become feasible to implement a microprocessor, a vector unit, and a multimegabyte bank-interleaved L2 cache on a single die. Parallel access to strided vectors on the L2 cache is a major performance issue on such vector microprocessors. A major difficulty for such a parallel access is that one would like to interleave the cache on a block size basis in order to benefit from spatial locality and to maintain a low tag volume, while strided vector accesses naturally work on a word granularity. In this paper, we address this issue. Considering a parallel vector unit with 2/sup n/ independent lanes, a 2/sup n/ bank interleaved cache, and a cache line size of 2/sup k/ words, we show that any slice of 2/sup n+k/ consecutive elements of any strided vector with stride 2/sup r/R with R odd and r /spl les/ k can be accessed in the L2 cache and routed back to the lanes in 2/sup k/ subslices of 2/sup n/ elements.

AN ANALYSIS OF NEURAL NETWORKS AS SIMULATORS AND EMULATORS

Recent advances in artificial neural networks (ANNs) have led to the design and construction of neuroarchitectures as simulator and emulators of a variety of problems in science and engineering. Such problems include pattern recognition, prediction, optimization, associative memory, and control of dynamic systems. This paper offers an analytical overview of the most successful design, implementation, and application of neuroarchitectures as neurosimulators and neuroemulators. It also outlines historical notes on the formulation of basic biological neuron, artificial computational models, network architectures, and learning processes of the most common ANN; describes and analyzes neurosimulation on parallel architecture both in software and hardware (neurohardware); presents the simulation of ANNs on parallel architectures; gives a brief introduction of ANNs in vector microprocessor systems; and presents ANNs in terms of the "new technologies". Specifically, it discusses cellular computing, cellular neural networks (CNNs), a new proposition for unsupervised neural networks (UNNs), and pulse coupled neural networks (PCNNs).

Spert-II: a vector microprocessor system

The Spert-II fixed point vector microprocessor system performs training and recall faster than commercial workstations for neural networks used in speech recognition research. We have packaged a prototype full custom vector microprocessor, TO, as the Spert-II (Synthetic Perceptron Testbed II) workstation accelerator system. We originally developed Spert-II to accelerate multiparameter neural network training for speech recognition research. Our speech research algorithms constantly change. Also, neural nets are often integrated with other tasks to form complete applications. We thus desired a general purpose, easily programmable accelerator that could speed up a range of tasks.