Heterogeneous Architectures For Parallel Acceleration

To enable a new generation of digital computing applications, the greatest challenge is to provide a better level of energy efficiency (intended as the performance that a system can provide within a certain power budget) without giving up a systems's flexibility. This constraint applies to digital system across all scales, starting from ultra-low power implanted devices up to datacenters for high-performance computing and for the "cloud". In this thesis, we show that architectural heterogeneity is the key to provide this efficiency and to respond to many of the challenges of tomorrow's computer architecture - and at the same time we show methodologies to introduce it with little or no loss in terms of flexibility. In particular, we show that heterogeneity can be employed to tackle the "walls" that impede further development of new computing applications: the utilization wall, i.e. the impossibility to keep all transistors on in deeply integrated chips, and the "data deluge", i.e. the amount of data to be processed that is scaling up much faster than the computing performance and efficiency. We introduce a methodology to improve heterogeneous design exploration of tightly coupled clusters; moreover we propose a fractal heterogeneity architecture that is a parallel accelerator for low-power sensor nodes, and is itself internally heterogeneous thanks to an heterogeneous coprocessor for brain-inspired computing. This platform, which is silicon-proven, can lead to more than 100x improvement in terms of energy efficiency with respect to typical computing nodes used within the same domain, enabling the application of complex algorithms, vastly more performance-hungry than the current state-of-the-art in the ULP computing domain

A sub-mW IoT-endnode for always-on visual monitoring and smart triggering

This work presents a fully-programmable Internet of Things (IoT) visual sensing node that targets sub-mW power consumption in always-on monitoring scenarios. The system features a spatial-contrast $128\mathrm{x}64$ binary pixel imager with focal-plane processing. The sensor, when working at its lowest power mode ($10\mu W$ at 10 fps), provides as output the number of changed pixels. Based on this information, a dedicated camera interface, implemented on a low-power FPGA, wakes up an ultra-low-power parallel processing unit to extract context-aware visual information. We evaluate the smart sensor on three always-on visual triggering application scenarios. Triggering accuracy comparable to RGB image sensors is achieved at nominal lighting conditions, while consuming an average power between $193\mu W$ and $277\mu W$, depending on context activity. The digital sub-system is extremely flexible, thanks to a fully-programmable digital signal processing engine, but still achieves 19x lower power consumption compared to MCU-based cameras with significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa

Power and Energy Aware Heterogeneous Computing Platform

During the last decade, wireless technologies have experienced significant development, most notably in the form of mobile cellular radio evolution from GSM to UMTS/HSPA and thereon to Long-Term Evolution (LTE) for increasing the capacity and speed of wireless data networks. Considering the real-time constraints of the new wireless standards and their demands for parallel processing, reconfigurable architectures and in particular, multicore platforms are part of the most successful platforms due to providing high computational parallelism and throughput. In addition to that, by moving toward Internet-of-Things (IoT), the number of wireless sensors and IP-based high throughput network routers is growing at a rapid pace. Despite all the progression in IoT, due to power and energy consumption, a single chip platform for providing multiple communication standards and a large processing bandwidth is still missing.The strong demand for performing different sets of operations by the embedded systems and increasing the computational performance has led to the use of heterogeneous multicore architectures with the help of accelerators for computationally-intensive data-parallel tasks acting as coprocessors. Currently, highly heterogeneous systems are the most power-area efficient solution for performing complex signal processing systems. Additionally, the importance of IoT has increased significantly the need for heterogeneous and reconfigurable platforms.On the other hand, subsequent to the breakdown of the Dennardian scaling and due to the enormous heat dissipation, the performance of a single chip was obstructed by the utilization wall since all cores cannot be clocked at their maximum operating frequency. Therefore, a thermal melt-down might be happened as a result of high instantaneous power dissipation. In this context, a large fraction of the chip, which is switched-off (Dark) or operated at a very low frequency (Dim) is called Dark Silicon. The Dark Silicon issue is a constraint for the performance of computers, especially when the up-coming IoT scenario will demand a very high performance level with high energy efficiency. Among the suggested solution to combat the problem of Dark-Silicon, the use of application-specific accelerators and in particular Coarse-Grained Reconfigurable Arrays (CGRAs) are the main motivation of this thesis work.This thesis deals with design and implementation of Software Defined Radio (SDR) as well as High Efficiency Video Coding (HEVC) application-specific accelerators for computationally intensive kernels and data-parallel tasks. One of the most important data transmission schemes in SDR due to its ability of providing high data rates is Orthogonal Frequency Division Multiplexing (OFDM). This research work focuses on the evaluation of Heterogeneous Accelerator-Rich Platform (HARP) by implementing OFDM receiver blocks as designs for proof-of-concept. The HARP template allows the designer to instantiate a heterogeneous reconfigurable platform with a very large amount of custom-tailored computational resources while delivering a high performance in terms of many high-level metrics. The availability of this platform lays an excellent foundation to investigate techniques and methods to replace the Dark or Dim part of chip with high-performance silicon dissipating very low power and energy. Furthermore, this research work is also addressing the power and energy issues of the embedded computing systems by tailoring the HARP for self-aware and energy-aware computing models. In this context, the instantaneous power dissipation and therefore the heat dissipation of HARP are mitigated on FPGA/ASIC by using Dynamic Voltage and Frequency Scaling (DVFS) to minimize the dark/dim part of the chip. Upgraded HARP for self-aware and energy-aware computing can be utilized as an energy-efficient general-purpose transceiver platform that is cognitive to many radio standards and can provide high throughput while consuming as little energy as possible. The evaluation of HARP has shown promising results, which makes it a suitable platform for avoiding Dark Silicon in embedded computing platforms and also for diverse needs of IoT communications.In this thesis, the author designed the blocks of OFDM receiver by crafting templatebased CGRA devices and then attached them to HARP’s Network-on-Chip (NoC) nodes. The performance of application-specific accelerators generated from templatebased CGRAs, the performance of the entire platform subsequent to integrating the CGRA nodes on HARP and the NoC traffic are recorded in terms of several highlevel performance metrics. In evaluating HARP on FPGA prototype, it delivers a performance of 0.012 GOPS/mW. Because of the scalability and regularity in HARP, the author considered its value as architectural constant. In addition to showing the gain and the benefits of maximizing the number of reconfigurable processing resources on a platform in comparison to the scaled performance of several state-of-the-art platforms, HARP’s architectural constant ensures application-independent figure of merit. HARP is further evaluated by implementing various sizes of Discrete Cosine transform (DCT) and Discrete Sine Transform (DST) dedicated for HEVC standard, which showed its ability to sustain Full HD 1080p format at 30 fps on FPGA. The author also integrated self-aware computing model in HARP to mitigate the power dissipation of an OFDM receiver. In the case of FPGA implementation, the total power dissipation of the platform showed 16.8% reduction due to employing the Feedback Control System (FCS) technique with Dynamic Frequency Scaling (DFS). Furthermore, by moving to ASIC technology and scaling both frequency and voltage simultaneously, significant dynamic power reduction (up to 82.98%) was achieved, which proved the DFS/DVFS techniques as one step forward to mitigate the Dark Silicon issue

Embedding Logic and Non-volatile Devices in CMOS Digital Circuits for Improving Energy Efficiency

abstract: Static CMOS logic has remained the dominant design style of digital systems for more than four decades due to its robustness and near zero standby current. Static CMOS logic circuits consist of a network of combinational logic cells and clocked sequential elements, such as latches and flip-flops that are used for sequencing computations over time. The majority of the digital design techniques to reduce power, area, and leakage over the past four decades have focused almost entirely on optimizing the combinational logic. This work explores alternate architectures for the flip-flops for improving the overall circuit performance, power and area. It consists of three main sections. First, is the design of a multi-input configurable flip-flop structure with embedded logic. A conventional D-type flip-flop may be viewed as realizing an identity function, in which the output is simply the value of the input sampled at the clock edge. In contrast, the proposed multi-input flip-flop, named PNAND, can be configured to realize one of a family of Boolean functions called threshold functions. In essence, the PNAND is a circuit implementation of the well-known binary perceptron. Unlike other reconfigurable circuits, a PNAND can be configured by simply changing the assignment of signals to its inputs. Using a standard cell library of such gates, a technology mapping algorithm can be applied to transform a given netlist into one with an optimal mixture of conventional logic gates and threshold gates. This approach was used to fabricate a 32-bit Wallace Tree multiplier and a 32-bit booth multiplier in 65nm LP technology. Simulation and chip measurements show more than 30% improvement in dynamic power and more than 20% reduction in core area. The functional yield of the PNAND reduces with geometry and voltage scaling. The second part of this research investigates the use of two mechanisms to improve the robustness of the PNAND circuit architecture. One is the use of forward and reverse body biases to change the device threshold and the other is the use of RRAM devices for low voltage operation. The third part of this research focused on the design of flip-flops with non-volatile storage. Spin-transfer torque magnetic tunnel junctions (STT-MTJ) are integrated with both conventional D-flipflop and the PNAND circuits to implement non-volatile logic (NVL). These non-volatile storage enhanced flip-flops are able to save the state of system locally when a power interruption occurs. However, manufacturing variations in the STT-MTJs and in the CMOS transistors significantly reduce the yield, leading to an overly pessimistic design and consequently, higher energy consumption. A detailed analysis of the design trade-offs in the driver circuitry for performing backup and restore, and a novel method to design the energy optimal driver for a given yield is presented. Efficient designs of two nonvolatile flip-flop (NVFF) circuits are presented, in which the backup time is determined on a per-chip basis, resulting in minimizing the energy wastage and satisfying the yield constraint. To achieve a yield of 98%, the conventional approach would have to expend nearly 5X more energy than the minimum required, whereas the proposed tunable approach expends only 26% more energy than the minimum. A non-volatile threshold gate architecture NV-TLFF are designed with the same backup and restore circuitry in 65nm technology. The embedded logic in NV-TLFF compensates performance overhead of NVL. This leads to the possibility of zero-overhead non-volatile datapath circuits. An 8-bit multiply-and- accumulate (MAC) unit is designed to demonstrate the performance benefits of the proposed architecture. Based on the results of HSPICE simulations, the MAC circuit with the proposed NV-TLFF cells is shown to consume at least 20% less power and area as compared to the circuit designed with conventional DFFs, without sacrificing any performance.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Neurostream: Scalable and Energy Efficient Deep Learning with Smart Memory Cubes

open4siHigh-performance computing systems are moving towards 2.5D and 3D memory hierarchies, based on High Bandwidth Memory (HBM) and Hybrid Memory Cube (HMC) to mitigate the main memory bottlenecks. This trend is also creating new opportunities to revisit near-memory computation. In this paper, we propose a flexible processor-in-memory (PIM) solution for scalable and energy-efficient execution of deep convolutional networks (ConvNets), one of the fastest-growing workloads for servers and high-end embedded systems. Our co-design approach consists of a network of Smart Memory Cubes (modular extensions to the standard HMC) each augmented with a many-core PIM platform called NeuroCluster. NeuroClusters have a modular design based on NeuroStream coprocessors (for Convolution-intensive computations) and general-purpose RISC-V cores. In addition, a DRAM-friendly tiling mechanism and a scalable computation paradigm are presented to efficiently harness this computational capability with a very low programming effort. NeuroCluster occupies only 8 percent of the total logic-base (LoB) die area in a standard HMC and achieves an average performance of 240 GFLOPS for complete execution of full-featured state-of-the-art (SoA) ConvNets within a power budget of 2.5 W. Overall 11 W is consumed in a single SMC device, with 22.5 GFLOPS/W energy-efficiency which is 3.5X better than the best GPU implementations in similar technologies. The minor increase in system-level power and the negligible area increase make our PIM system a cost-effective and energy efficient solution, easily scalable to 955 GFLOPS with a small network of just four SMCs.openAzarkhish, Erfan*; Rossi, Davide; Loi, Igor; Benini, LucaAzarkhish, Erfan*; Rossi, Davide; Loi, Igor; Benini, Luc

Monitor amb control strategies to reduce the impact of process variations in digital circuits

As CMOS technology scales down, Process, Voltage, Temperature and Ageing (PVTA) variations have an increasing impact on the performance and power consumption of electronic devices. These issues may hold back the continuous improvement of these devices in the near future. There are several ways to face the variability problem: to increase the operating margins of maximum clock frequency, the implementation of lithographic friendly layout styles, and the last one and the focus of this thesis, to adapt the circuit to its actual manufacturing and environment conditions by tuning some of the adjustable parameters once the circuit has been manufactured. The main challenge of this thesis is to develop a low-area variability compensation mechanism to automatically mitigate PVTA variations in run-time, i.e. while integrated circuit is running. This implies the development of a sensor to obtain the most accurate picture of variability, and the implementation of a control block to knob some of the electrical parameters of the circuit.A mesura que la tecnologia CMOS escala, les variacions de Procés, Voltatge, Temperatura i Envelliment (PVTA) tenen un impacte creixent en el rendiment i el consum de potència dels dispositius electrònics. Aquesta problemàtica podria arribar a frenar la millora contínua d'aquests dispositius en un futur proper. Hi ha diverses maneres d'afrontar el problema de la variabilitat: relaxar el marge de la freqüència màxima d'operació, implementar dissenys físics de xips més fàcils de litografiar, i per últim i com a tema principal d'aquesta tesi, adaptar el xip a les condicions de fabricació i d'entorn mitjançant la modificació d'algun dels seus paràmetres ajustables una vegada el circuit ja ha estat fabricat. El principal repte d'aquesta tesi és desenvolupar un mecanisme de compensació de variabilitat per tal de mitigar les variacions PVTA de manera automàtica en temps d'execució, és a dir, mentre el xip està funcionant. Això implica el desenvolupament d'un sensor capaç de mesurar la variabilitat de la manera més acurada possible, i la implementació d'un bloc de control que permeti l'ajust d'alguns dels paràmetres elèctrics dels circuits