•Applications ranging from scientific computing to machine learning can have extremely sparse inputs

With the slowing of Moore’s law, computer architects have turned to domain-specific hardware specialization to continue improving the performance and efficiency of computing systems. However, specialization typically entails significant modifications to the software stack to properly leverage the updated hardware. The lack of a structured approach for updating the compiler and the accelerator in tandem has impeded many attempts to systematize this procedure. We propose a new approach to enable flexible and evolvable domain-specific hardware specialization based on coarse-grained reconfigurable arrays (CGRAs). Our agile methodology employs a combination of new programming languages and formal methods to automatically generate the accelerator hardware and its compiler from a single source of truth. This enables the creation of design-space exploration frameworks that automatically generate accelerator architectures that approach the efficiencies of hand-designed accelerators, with a significantly lower design effort for both hardware and compiler generation. Our current system accelerates dense linear algebra applications but is modular and can be extended to support other domains. Our methodology has the potential to significantly improve the productivity of hardware-software engineering teams and enable quicker customization and deployment of complex accelerator-rich computing systems.

To effectively minimize static power for a wide range of applications, power domains for coarse-grained reconfigurable array (CGRA) architectures need to be more fine-grained than those found in a typical application-specific integrated circuit. However, the special isolation logic needed to ensure electrical protection between off and on domains makes fine-grained power domains area- and timing-inefficient. We propose a novel design of the CGRA routing fabric that reduces the area overhead of power domain boundary protection from around 9% to less than 1% without incurring any extra timing delay from the isolation cells. Conventional Unified Power Format based flow for power domain boundary protection does not support this design choice. Therefore, we create our own compiler-like passes that iteratively introduce the needed design changes, and formally verify the transformations using methods based on satisfiability modulo theories. These passes also let us optimize how we handle test and debug signals through the off tiles in the CGRA. Using our framework, we add power domains to a CGRA that we designed and taped out. The CGRA has 32 × 16 processing element and memory tiles and 4-MB secondary memory. We address the implementation challenges encountered due to the introduction of fine-grained power domains, including the addressing of the CGRA tiles, the power grid design, well substrate connections, and distribution of global signals. Our CGRA achieves up to 83% reduction in leakage power and 26% reduction in total power versus an identical CGRA without multiple power domains, for a range of image processing and machine learning applications.

Achieving high code reuse in physical design flows is challenging but increasingly necessary to build complex systems. Unfortunately, existing approaches based on parameterized Tcl generators support very limited reuse as designers customize flows for specific designs and technologies, preventing their reuse in future flows. We present a vision and framework based on modular flow generators that encapsulates coarse-grained and fine-grained reusable code in modular nodes and assembles them into complete flows. The key feature is a flow consistency and instrumentation layer embedded in Python, which supports mechanisms for rapid and early feedback on inconsistent composition. We evaluate the design flows of successive generations of silicon prototypes built in TSMC16, TSMC28, TSMC40, SKY130, and IBM180 technologies, showing how our approach can enable significant code reuse in future flows.

Dedicated hardware accelerators popular for imaging, vision, and machine learning (ML) applications

Reconfigurable accelerator fabrics, including coarse-grain reconfigurable arrays (CGRAs), have experienced a resurgence in interest because they allow fast-paced software algorithm development to continue evolving post-fabrication. CGRAs traditionally target regular workloads with data-level parallelism (e.g., neural networks, image processing), but once integrated into an SoC they remain idle and unused for irregular workloads. An emerging trend towards repurposing these idle resources raises important questions for how to efficiently map and execute general-purpose loops which may have irregular memory accesses, irregular control flow, and inter-iteration loop dependencies. Recent work has increasingly leveraged elasticity in CGRAs to mitigate the first two challenges, but elasticity alone does not address inter-iteration loop dependencies which can easily bottleneck overall performance. In this paper, we address all three challenges for irregular loop specialization and propose ultra-elastic CGRAs (UE-CGRAs), a novel elastic CGRA that accelerates true-dependency bottlenecks and saves energy in irregular loops by overcoming traditional VLSI challenges. UE-CGRAs allow configurable fine-grain dynamic voltage and frequency scaling (DVFS) for each of potentially hundreds of tiny processing elements (PEs) in the CGRA, enabling chains of connected PEs to “rest” at lower voltages and frequencies to save energy, while other chains of connected PEs can “sprint” at higher voltages and frequencies to accelerate through true-dependency bottlenecks. UE-CGRAs rely on a novel ratiochronous clocking scheme carefully overlaid on the inter-PE elastic interconnect to enable low-latency crossings while remaining fully verifiable with commercial static timing analysis tools. We present the UE-CGRA analytical model, compiler, architectural template, and VLSI circuitry, and we demonstrate how UE-CGRAs can specialize for irregular loops and improve performance ($ 1.42-1.50\times$) or energy efficiency $(1.24-2.32\times)$ with reasonable area overhead compared to traditional inelastic and elastic CGRAs, while also improving performance ($ 1.35-3.38\times$) or energy efficiency (up to $1.53\times$) compared to a RISC-V core.

Although an agile approach is standard for software design, how to properly adapt this method to hardware is still an open question. This work addresses this question while building a system on chip (SoC) with specialized accelerators. Rather than using a traditional waterfall design flow, which starts by studying the application to be accelerated, we begin by constructing a complete flow from an application expressed in a high-level domain-specific language (DSL), in our case Halide, to a generic coarse-grained reconfigurable array (CGRA). As our under-standing of the application grows, the CGRA design evolves, and we have developed a suite of tools that tune application code, the compiler, and the CGRA to increase the efficiency of the resulting implementation. To meet our continued need to update parts of the system while maintaining the end-to-end flow, we have created DSL-based hardware generators that not only provide the Verilog needed for the implementation of the CGRA, but also create the collateral that the compiler/mapper/place and route system needs to configure its operation. This work provides a systematic approach for desiging and evolving high-performance and energy-efficient hardware-software systems for any application domain.

There is a growing interest in the open-source hardware movement to amortize non-recurring engineering costs by using plug-and-play system-on-chip (SoC) designs, where the communication among different components is provided by an on-chip interconnection network. Unfortunately, building an on-chip network (OCN) that is suitable for a specific SoC design requires the exploration of a large number of design options and involves diverse research methodologies to evaluate performance, area, energy, and timing. In this paper, we propose PyOCN, a unified framework that vertically integrates multiple research methodologies to enable productively exploring the OCN design space. PyOCN is the first comprehensive framework for modeling (e.g., functional-level, cycle-level, and register-transfer-level), testing (e.g., unit testing, integration testing, and property-based random testing), and evaluating (e.g., simulating, generating, and characterizing) on-chip interconnection networks. We use a case study based on a 64-terminal butterfly network to illustrate the key features of PyOCN and to demonstrate the framework's potential in productively modeling, testing, and evaluating OCNs.

A high-productivity digital VLSI flow for designing complex SoCs is presented. The flow includes high-level synthesis tools, an object-oriented library of synthesizable SystemC and C++ components, and a modular VLSI physical design approach based on fine-grained globally asynchronous locally synchronous (GALS) clocking. The flow was demonstrated on a 16nm FinFET testchip targeting machine learning and computer vision.

Task-based parallel programming frameworks offer compelling productivity and performance benefits for modern chip multi-processors (CMPs). At the same time, CMPs also provide packed-SIMD units to exploit fine-grain data parallelism. Two fundamental challenges make using packed-SIMD units with task-parallel programs particularly difficult: (1) the intra-core parallel abstraction gap; and (2) inefficient execution of irregular tasks. To address these challenges, we propose augmenting CMPs with intra-core loop-task accelerators (LTAs). We introduce a lightweight hint in the instruction set to elegantly encode loop-task execution and an LTA microarchitectural template that can be configured at design time for different amounts of spatial/temporal decoupling to efficiently execute both regular and irregular loop tasks. Compared to an in-order CMP baseline, CMP+LTA results in an average speedup of 4.2X (1.8X area normalized) and similar energy efficiency. Compared to an out-of-order CMP baseline, CMP+LTA results in an average speedup of 2.3X (1.5X area normalized) and also improves energy efficiency by 3.2X. Our work suggests augmenting CMPs with lightweight LTAs can improve performance and efficiency on both regular and irregular loop-task parallel programs with minimal software changes.

This poster will describe a taped-out 2×2mm 1.3 M-transistor test chip in IBM 130 nm designed using our new Python-based hardware modeling framework. The goal of our tapeout was to demonstrate the ability of this framework to enable Agile hardware design flows.

Amdahl's law provides architects a compelling reason to introduce system asymmetry to optimize for both serial and parallel regions of execution. Asymmetry in a multicore processor can arise statically (e.g., from core microarchitecture) or dynamically (e.g., applying dynamic voltage/frequency scaling). Work stealing is an increasingly popular approach to task distribution that elegantly balances task-based parallelism across multiple worker threads. In this paper, we propose asymmetry-aware work-stealing (AAWS) runtimes, which are carefully designed to exploit both the static and dynamic asymmetry in modern systems. AAWS runtimes use three key hardware/software techniques: work-pacing, work-sprinting, and work-mugging. Work-pacing and work-sprinting are novel techniques that combine a marginal-utility-based approach with integrated voltage regulators to improve performance and energy efficiency in high-and low-parallel regions. Work-mugging is a previously proposed technique that enables a waiting big core to preemptively migrate work from a busy little core. We propose a simple implementation of work-mugging based on lightweight user-level interrupts. We use a vertically integrated research methodology spanning software, architecture, and VLSI to make the case that holistically combining static asymmetry, dynamic asymmetry, and work-stealing runtimes can improve both performance and energy efficiency in future multicore systems.

Recent work has shown that monolithic integration of voltage regulators will be feasible in the near future, enabling reduced system cost and the potential for fine-grain voltage scaling (FGVS). More specifically, on-chip switched-capacitor regulators appear to offer an attractive trade-off in terms of integration complexity, power density, power efficiency, and response time. In this paper, we use architecture-level modeling to explore a new dynamic voltage/frequency scaling controller called the fine-grain synchronization controller (FG-SYNC+). FG-SYNC+ enables improved performance and energy efficiency at similar average power for multithreaded applications with activity imbalance. We then use circuit-level modeling to explore various approaches to organizing on-chip voltage regulation, including a new approach called reconfigurable power distribution networks (RPDNs). RPDNs allow one regulator to "borrow" energy storage from regulators associated with underutilized cores resulting in improved area/power efficiency and faster response times. We evaluate FG-SYNC+ and RPDN using a vertically integrated research methodology, and our results demonstrate a 10-50% performance and 10-70% energy-efficiency improvement on the majority of the applications studied compared to no FGVS, yet RPDN uses 40% less area compared to a more traditional per-core regulation scheme.

SIMT architectures improve performance and efficiency by exploiting control and memory-access structure across data-parallel threads. Value structure occurs when multiple threads operate on values that can be compactly encoded, e.g., by using a simple function of the thread index. We characterize the availability of control, memory-access, and value structure in typical kernels and observe ample amounts of value structure that is largely ignored by current SIMT architectures. We propose three microarchitectural mechanisms to exploit value structure based on compact affine execution of arithmetic, branch, and memory instructions. We explore these mechanisms within the context of traditional SIMT microarchitectures (GP-SIMT), found in general-purpose graphics processing units, as well as fine-grain SIMT microarchitectures (FG-SIMT), a SIMT variant appropriate for compute-focused data-parallel accelerators. Cycle-level modeling of a modern GP-SIMT system and a VLSI implementation of an eight-lane FG-SIMT execution engine are used to evaluate a range of application kernels. When compared to a baseline without compact affine execution, our approach can improve GP-SIMT cycle-level performance by 4-17% and can improve FG-SIMT absolute performance by 20-65% and energy efficiency up to 30% for a majority of the kernels.