Harnessing Simulation Acceleration to Solve the Digital Design Verification Challenge.

Today, design verification is by far the most resource and time-consuming activity of any new digital integrated circuit development. Within this area, the vast majority of the verification effort in industry relies on simulation platforms, which are implemented either in hardware or software. A "simulator" includes a model of each component of a design and has the capability of simulating its behavior under any input scenario provided by an engineer. Thus, simulators are deployed to evaluate the behavior of a design under as many input scenarios as possible and to identify and debug all incorrect functionality. Two features are critical in simulators for the validation effort to be effective: performance and checking/debugging capabilities. A wide range of simulator platforms are available today: on one end of the spectrum there are software-based simulators, providing a very rich software infrastructure for checking and debugging the design's functionality, but executing only at 1-10 simulation cycles per second (while actual chips operate at GHz speeds). At the other end of the spectrum, there are hardware-based platforms, such as accelerators, emulators and even prototype silicon chips, providing higher performances by 4 to 9 orders of magnitude, at the cost of very limited or non-existent checking/debugging capabilities. As a result, today, simulation-based validation is crippled: one can either have satisfactory performance on hardware-accelerated platforms or critical infrastructures for checking/debugging on software simulators, but not both. This dissertation brings together these two ends of the spectrum by presenting solutions that offer high-performance simulation with effective checking and debugging capabilities. Specifically, it addresses the performance challenge of software simulators by leveraging inexpensive off-the-shelf graphics processors as massively parallel execution substrates, and then exposing the parallelism inherent in the design model to that architecture. For hardware-based platforms, the dissertation provides solutions that offer enhanced checking and debugging capabilities by abstracting the relevant data to be logged during simulation so to minimize the cost of collection, transfer and processing. Altogether, the contribution of this dissertation has the potential to solve the challenge of digital design verification by enabling effective high-performance simulation-based validation.PHDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99781/1/dchatt_1.pd

Design of OpenCL-compatible multithreaded hardware accelerators with dynamic support for embedded FPGAs

ARTICo3 is an architecture that permits to dynamically set an arbitrary number of reconfigurable hardware accelerators, each containing a given number of threads fixed at design time according to High Level Synthesis constraints. However, the replication of these modules can be decided at runtime to accelerate kernels by increasing the overall number of threads, add modular redundancy to increase fault tolerance, or any combination of the previous. An execution scheduler is used at kernel invocation to deliver the appropriate data transfers, optimizing memory transactions, and sequencing or parallelizing execution according to the configuration specified by the resource manager of the architecture. The model of computation is compatible with the OpenCL kernel execution model, and memory transfers and architecture are arranged to match the same optimization criteria as for kernel execution in GPU architectures but, differently to other approaches, with dynamic hardware execution support. In this paper, a novel design methodology for multithreaded hardware accelerators is presented. The proposed framework provides OpenCL compatibility by implementing a memory model based on shared memory between host and compute device, which removes the overhead imposed by data transferences at global memory level, and local memories inside each accelerator, i.e. compute unit, which are connected to global memory through optimized DMA links. These local memories provide unified access, i.e. a continuous memory map, from the host side, but are divided in a configurable number of independent banks (to increase available ports) from the processing elements side to fully exploit data-level parallelism. Experimental results show OpenCL model compliance using multithreaded hardware accelerators and enhanced dynamic adaptation capabilities

SECDA-TFLite: a toolkit for efficient development of FPGA-based DNN accelerators for edge inference

In this paper we propose SECDA-TFLite, a new open source toolkit for developing DNN hardware accelerators, integrated within the TFLite framework. The toolkit leverages the principles of SECDA, a hardware/software co-design methodology, to reduce the design time of optimized DNN inference accelerators on edge devices with FPGAs. With SECDA-TFLite, we reduce the initial setup costs associated with integrating a new accelerator design within a target DNN framework, allowing developers to focus on the design. SECDA-TFLite also includes modules for cost-effective SystemC simulation, profiling, and AXI-based data communication. As a case study, we use SECDA-TFLite to develop and evaluate three accelerator designs across seven common CNN models and two BERT-based models against an ARM A9 CPU-only baseline, achieving an average performance speedup across models of up to 3.4× for the CNN models and of up to 2.5× for the BERT-based models. Our code is available at https://github.com/gicLAB/SECDA-TFLite

Towards hardware as a reconfigurable, elastic, and specialized service

As modern Data Center workloads become increasingly complex, constrained, and critical, mainstream CPU-centric computing has had ever more difficulty in keeping pace. Future data centers are moving towards a more fluid and heterogeneous model, with computation and communication no longer localized to commodity CPUs and routers. Next generation data-centric Data Centers will compute everywhere, whether data is stationary (e.g. in memory) or on the move (e.g. in network). While deploying FPGAs in NICS, as co-processors, in the router, and in Bump-in-the-Wire configurations is a step towards implementing the data-centric model, it is only part of the overall solution. The other part is actually leveraging this reconfigurable hardware. For this to happen, two problems must be addressed: code generation and deployment generation. By code generation we mean transforming abstract representations of an algorithm into equivalent hardware. Deployment generation refers to the runtime support needed to facilitate the execution of this hardware on an FPGA. Efforts at creating supporting tools in these two areas have thus far provided limited benefits. This is because the efforts are limited in one or more of the following ways: They i) do not provide fundamental solutions to a number of challenges, which makes them useful only to a limited group of (mostly) hardware developers, ii) are constrained in their scope, or iii) are ad hoc, i.e., specific to a single usage context, FPGA vendor, or Data Center configuration. Moreover, efforts in these areas have largely been mutually exclusive, which results in incompatibility across development layers; this requires wrappers to be designed to make interfaces compatible. As a result there is significant complexity and effort required to code and deploy efficient custom hardware for FPGAs; effort that may be orders-of-magnitude greater than for analogous software environments. The goal of this dissertation is to create a framework that enables reconfigurable logic in Data Centers to be targeted with the same level of effort as for a single CPU core. The underlying mechanism to this is a framework, which we refer to as Hardware as a Reconfigurable, Elastic and Specialized Service, or HaaRNESS. In this dissertation, we address two of the core challenges of HaaRNESS: reducing the complexity of code generation by constraining High Level Synthesis (HLS) toolflows, and replacing ad hoc models of deployment generation by generalizing and formalizing what is needed for a hardware Operating System. These parts are unified by the back-end of HLS toolflows which link generated compute pipelines with the operating system, and provide appropriate APIs, wrappers, and software runtimes. The contributions of this dissertation are the following: i) an empirically guided set of systematic transformations for generating high quality HLS code; ii) a framework for instrumenting HLS compiler to identify and remove optimization blockers; iii) a framework for RTL simulation and IP generation of HLS kernels for rapid turnaround; and iv) a framework for generalization and formalization of hardware operating systems to address the {\it ad hoc}'ness of existing deployment generation and ensure uniform structure and APIs

Bridging Python to Silicon: The SODA Toolchain

Systems performing scientific computing, data analysis, and machine learning tasks have a growing demand for application-specific accelerators that can provide high computational performance while meeting strict size and power requirements. However, the algorithms and applications that need to be accelerated are evolving at a rate that is incompatible with manual design processes based on hardware description languages. Agile hardware design tools based on compiler techniques can help by quickly producing an application specific integrated circuit (ASIC) accelerator starting from a high-level algorithmic description. We present the SODA Synthesizer, a modular and open-source hardware compiler that provides automated end-to-end synthesis from high-level software frameworks to ASIC implementation, relying on multi-level representations to progressively lower and optimize the input code. Our approach does not require the application developer to write register-transfer level code, and it is able to reach up to 364 GFLOPS/W efficiency (32-bit precision) on typical convolutional neural network operators

Advances in Architectures and Tools for FPGAs and their Impact on the Design of Complex Systems for Particle Physics

The continual improvement of semiconductor technology has provided rapid advancements in device frequency and density. Designers of electronics systems for high-energy physics (HEP) have benefited from these advancements, transitioning many designs from fixed-function ASICs to more flexible FPGA-based platforms. Today’s FPGA devices provide a significantly higher amount of resources than those available during the initial Large Hadron Collider design phase. To take advantage of the capabilities of future FPGAs in the next generation of HEP experiments, designers must not only anticipate further improvements in FPGA hardware, but must also adopt design tools and methodologies that can scale along with that hardware. In this paper, we outline the major trends in FPGA hardware, describe the design challenges these trends will present to developers of HEP electronics, and discuss a range of techniques that can be adopted to overcome these challenges

A configurable vector processor for accelerating speech coding algorithms

The growing demand for voice-over-packer (VoIP) services and multimedia-rich applications has made increasingly important the efficient, real-time implementation of low-bit rates speech coders on embedded VLSI platforms. Such speech coders are designed to substantially reduce the bandwidth requirements thus enabling dense multichannel gateways in small form factor. This however comes at a high computational cost which mandates the use of very high performance embedded processors. This thesis investigates the potential acceleration of two major ITU-T speech coding algorithms, namely G.729A and G.723.1, through their efficient implementation on a configurable extensible vector embedded CPU architecture. New scalar and vector ISAs were introduced which resulted in up to 80% reduction in the dynamic instruction count of both workloads. These instructions were subsequently encapsulated into a parametric, hybrid SISD (scalar processor)–SIMD (vector) processor. This work presents the research and implementation of the vector datapath of this vector coprocessor which is tightly-coupled to a Sparc-V8 compliant CPU, the optimization and simulation methodologies employed and the use of Electronic System Level (ESL) techniques to rapidly design SIMD datapaths

Degradation in FPGAs: Monitoring, Modeling and Mitigation

This dissertation targets the transistor aging degradation as well as the associated thermal challenges in FPGAs (since there is an exponential relation between aging and chip temperature). The main objectives are to perform experimentation, analysis and device-level model abstraction for modeling the degradation in FPGAs, then to monitor the FPGA to keep track of aging rates and ultimately to propose an aging-aware FPGA design flow to mitigate the aging

ACiS: smart switches with application-level acceleration

Network performance has contributed fundamentally to the growth of supercomputing over the past decades. In parallel, High Performance Computing (HPC) peak performance has depended, first, on ever faster/denser CPUs, and then, just on increasing density alone. As operating frequency, and now feature size, have levelled off, two new approaches are becoming central to achieving higher net performance: configurability and integration. Configurability enables hardware to map to the application, as well as vice versa. Integration enables system components that have generally been single function-e.g., a network to transport data—to have additional functionality, e.g., also to operate on that data. More generally, integration enables compute-everywhere: not just in CPU and accelerator, but also in network and, more specifically, the communication switches. In this thesis, we propose four novel methods of enhancing HPC performance through Advanced Computing in the Switch (ACiS). More specifically, we propose various flexible and application-aware accelerators that can be embedded into or attached to existing communication switches to improve the performance and scalability of HPC and Machine Learning (ML) applications. We follow a modular design discipline through introducing composable plugins to successively add ACiS capabilities. In the first work, we propose an inline accelerator to communication switches for user-definable collective operations. MPI collective operations can often be performance killers in HPC applications; we seek to solve this bottleneck by offloading them to reconfigurable hardware within the switch itself. We also introduce a novel mechanism that enables the hardware to support MPI communicators of arbitrary shape and that is scalable to very large systems. In the second work, we propose a look-aside accelerator for communication switches that is capable of processing packets at line-rate. Functions requiring loops and states are addressed in this method. The proposed in-switch accelerator is based on a RISC-V compatible Coarse Grained Reconfigurable Arrays (CGRAs). To facilitate usability, we have developed a framework to compile user-provided C/C++ codes to appropriate back-end instructions for configuring the accelerator. In the third work, we extend ACiS to support fused collectives and the combining of collectives with map operations. We observe that there is an opportunity of fusing communication (collectives) with computation. Since the computation can vary for different applications, ACiS support should be programmable in this method. In the fourth work, we propose that switches with ACiS support can control and manage the execution of applications, i.e., that the switch be an active device with decision-making capabilities. Switches have a central view of the network; they can collect telemetry information and monitor application behavior and then use this information for control, decision-making, and coordination of nodes. We evaluate the feasibility of ACiS through extensive RTL-based simulation as well as deployment in an open-access cloud infrastructure. Using this simulation framework, when considering a Graph Convolutional Network (GCN) application as a case study, a speedup of on average 3.4x across five real-world datasets is achieved on 24 nodes compared to a CPU cluster without ACiS capabilities