The new era of cognitive computing brings forth the grand challenge of developing systems capable of processing massive amounts of noisy multisensory data. This type of intelligent computing poses a set of constraints, including real-time operation, low-power consumption and scalability, which require a radical departure from conventional system design. Brain-inspired architectures offer tremendous promise in this area. To this end, we developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture. With 4096 neurosynaptic cores, the TrueNorth chip contains 1 million digital neurons and 256 million synapses tightly interconnected by an event-driven routing infrastructure. The fully digital 5.4 billion transistor implementation leverages existing CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. With such aggressive design metrics and the TrueNorth architecture breaking path with prevailing architectures, it is clear that conventional computer-aided design (CAD) tools could not be used for the design. As a result, we developed a novel design methodology that includes mixed asynchronous-synchronous circuits and a complete tool flow for building an event-driven, low-power neurosynaptic chip. The TrueNorth chip is fully configurable in terms of connectivity and neural parameters to allow custom configurations for a wide range of cognitive and sensory perception applications. To reduce the system's communication energy, we have adapted existing application-agnostic very large-scale integration CAD placement tools for mapping logical neural networks to the physical neurosynaptic core locations on the TrueNorth chips. With that, we have successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition, with higher performance and orders of magnitude lower power consumption than the same algorithms run on von Neumann architectures. The TrueNorth chip and its tool flow serve as building blocks for future cognitive systems, and give designers an opportunity to develop novel brain-inspired architectures and systems based on the knowledge obtained from this paper.

Drawing on neuroscience, we have developed a parallel, event-driven kernel for neurosynaptic computation, that is efficient with respect to computation, memory, and communication. Building on the previously demonstrated highly optimized software expression of the kernel, here, we demonstrate True North, a co-designed silicon expression of the kernel. True North achieves five orders of magnitude reduction in energy to-solution and two orders of magnitude speedup in time-to solution, when running computer vision applications and complex recurrent neural network simulations. Breaking path with the von Neumann architecture, True North is a 4,096 core, 1 million neuron, and 256 million synapse brain-inspired neurosynaptic processor, that consumes 65mW of power running at real-time and delivers performance of 46 Giga-Synaptic OPS/Watt. We demonstrate seamless tiling of True North chips into arrays, forming a foundation for cortex-like scalability. True North's unprecedented time-to-solution, energy-to-solution, size, scalability, and performance combined with the underlying flexibility of the kernel enable a broad range of cognitive applications.

In recent years, real-time analytics has seen widespread adoption in the business world. While it provides useful business insights and improved market responsiveness, it also adds a computational burden to traditional online transaction processing (OLTP) systems. Analytics queries involve complex database operations such as sort, aggregation, and join that consume significant computational resources, and, when executed on the same system, may affect the performance of OLTP queries. In this paper, we try to address this issue by accelerating two such database operations, namely, projection and sort, using a field programmable gate array (FPGA). Our prototype is implemented on an Alter a Stratix V FPGA and achieves an order of magnitude speedup in the sort operation compared to baseline software. Furthermore, our prototype implements projection in parallel with other query operations on FPGA, thus completely eliminating the cost of projection without consuming any extra cycles on the FPGA. FPGA accelerated sort and projection have been integrated with our previous work on accelerating other query operations [1], making our analytics acceleration prototype on FPGA applicable to a wider variety of queries.

Business growth and technology advancements have resulted in growing amounts of enterprise data. To gain valuable business insight and competitive advantage, businesses demand the capability of performing real-time analytics on such data. This, however, involves expensive query operations that are very time consuming on traditional CPUs. Additionally, in traditional database management systems (DBMS), the CPU resources are dedicated to mission-critical transactional workloads. Offloading expensive analytics query operations to a co-processor can allow efficient execution of analytics workloads in parallel with transactional workloads.

Software based tools for simulation are not keeping up with the demands for increased chip and system design complexity. In this paper, we describe a cycle-accurate and cycle-reproducible large-scale FPGA platform that is designed from the ground up to accelerate logic verification of the Bluegene/Q compute node ASIC, a multi-processor SOC implemented in IBM's 45 nm SOI CMOS technology. This paper discusses the challenges for constructing such large-scale FPGA platforms, including design partitioning, clocking & synchronization, and debugging support, as well as our approach for addressing these challenges without sacrificing cycle accuracy and cycle reproducibility. The resulting fullchip simulation of the Bluegene/Q compute node ASIC runs at a simulated processor clock speed of 4 MHz, over 100,000 times faster than the logic level software simulation of the same design. The vast increase in simulation speed provides a new capability in the design cycle that proved to be instrumental in logic verification as well as early software development and performance validation for Bluegene/Q.

FPGAs have become indispensible in processor design, bring-up and debug. Traditionally FPGAs have been used in prototyping, allowing end-users to emulate functionality of a specific component of a processor. However, as the complexity of processors grows, another aspect of processor design, RTL verification, has become a prime target for acceleration using FPGAs. Software-only RTL simulation and verification tools are no longer sufficient for many verification tasks as they often incur long execution time penalties. Software simulation time for a basic Linux kernel bring-up on a BlueGene/Q [1] processor, with 16 user PowerPC A2 cores, for example, could easily exceed several years.

Loss less compression is often used before writing data to a storage medium or transmitting across a transmission medium. Compression aids by saving storage space or transmission bandwidth, a decompression operation is performed when the data is subsequently read. Though this scheme has clear benefits, the execution time of compression and decompression is critical to its application in real-time systems. Software compression utilities are often slow, leading to degraded system performance. Hardware-based solutions, on the other hand, often drive large resource requirements and are not amenable to supporting future algorithmic changes. In the current article, we present a high-throughput, streaming, loss less compression algorithm and its efficient implementation on FPGAs. The proposed solution provides a peak throughput of 1GB/sec per engine, with a sustained overall measured throughput of 2.66GB/sec on a PCIe-based FPGA board with two compression and two decompression engines. This result represents an overall speedup of 13.6× over reference software implementation. The proposed design is very lean, and, with multiple engines running in parallel, provides a path to potential speedups of up to two orders of magnitude. In the current implementation, the achievable overall throughput is limited only by the available PCIe bus bandwidth.