Survey on Combinatorial Register Allocation and Instruction Scheduling

Register allocation (mapping variables to processor registers or memory) and instruction scheduling (reordering instructions to increase instruction-level parallelism) are essential tasks for generating efficient assembly code in a compiler. In the last three decades, combinatorial optimization has emerged as an alternative to traditional, heuristic algorithms for these two tasks. Combinatorial optimization approaches can deliver optimal solutions according to a model, can precisely capture trade-offs between conflicting decisions, and are more flexible at the expense of increased compilation time. This paper provides an exhaustive literature review and a classification of combinatorial optimization approaches to register allocation and instruction scheduling, with a focus on the techniques that are most applied in this context: integer programming, constraint programming, partitioned Boolean quadratic programming, and enumeration. Researchers in compilers and combinatorial optimization can benefit from identifying developments, trends, and challenges in the area; compiler practitioners may discern opportunities and grasp the potential benefit of applying combinatorial optimization

Architecture Design Space Exploration for Streaming Applications Through Timing Analysis

In this paper we compare the maximum achievable throughput of different memory organisations of the processing elements that constitute a multiprocessor system on chip. This is done by modelling the mapping of a task with input and output channels on a processing element as a homogeneous synchronous dataflow graph, and use maximum cycle mean analysis to derive the throughput. In a HiperLAN2 case study we show how these techniques can be used to derive the required clock frequency and communication latencies in order to meet the application's throughput requirement on a multiprocessor system on chip that has one of the investigated memory organisations

clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions

Depthwise convolution and grouped convolution has been successfully applied to improve the efficiency of convolutional neural network (CNN). We suggest that these models can be considered as special cases of a generalized convolution operation, named channel local convolution(CLC), where an output channel is computed using a subset of the input channels. This definition entails computation dependency relations between input and output channels, which can be represented by a channel dependency graph(CDG). By modifying the CDG of grouped convolution, a new CLC kernel named interlaced grouped convolution (IGC) is created. Stacking IGC and GC kernels results in a convolution block (named CLC Block) for approximating regular convolution. By resorting to the CDG as an analysis tool, we derive the rule for setting the meta-parameters of IGC and GC and the framework for minimizing the computational cost. A new CNN model named clcNet is then constructed using CLC blocks, which shows significantly higher computational efficiency and fewer parameters compared to state-of-the-art networks, when being tested using the ImageNet-1K dataset. Source code is available at https://github.com/dqzhang17/clcnet.torch

Hierarchical clustered register file organization for VLIW processors

Technology projections indicate that wire delays will become one of the biggest constraints in future microprocessor designs. To avoid long wire delays and therefore long cycle times, processor cores must be partitioned into components so that most of the communication is done locally. In this paper, we propose a novel register file organization for VLIW cores that combines clustering with a hierarchical register file organization. Functional units are organized in clusters, each one with a local first level register file. The local register files are connected to a global second level register file, which provides access to memory. All intercluster communications are done through the second level register file. This paper also proposes MIRS-HC, a novel modulo scheduling technique that simultaneously performs instruction scheduling, cluster selection, inserts communication operations, performs register allocation and spill insertion for the proposed organization. The results show that although more cycles are required to execute applications, the execution time is reduced due to a shorter cycle time. In addition, the combination of clustering and hierarchy provides a larger design exploration space that trades-off performance and technology requirements.Peer ReviewedPostprint (published version

Power and memory optimization techniques in embedded systems design

Embedded systems incur tight constraints on power consumption and memory (which impacts size) in addition to other constraints such as weight and cost. This dissertation addresses two key factors in embedded system design, namely minimization of power consumption and memory requirement. The first part of this dissertation considers the problem of optimizing power consumption (peak power as well as average power) in high-level synthesis (HLS). The second part deals with memory usage optimization mainly targeting a restricted class of computations expressed as loops accessing large data arrays that arises in scientific computing such as the coupled cluster and configuration interaction methods in quantum chemistry. First, a mixed-integer linear programming (MILP) formulation is presented for the scheduling problem in HLS using multiple supply-voltages in order to optimize peak power as well as average power and energy consumptions. For large designs, the MILP formulation may not be suitable; therefore, a two-phase iterative linear programming formulation and a power-resource-saving heuristic are presented to solve this problem. In addition, a new heuristic that uses an adaptation of the well-known force-directed scheduling heuristic is presented for the same problem. Next, this work considers the problem of module selection simultaneously with scheduling for minimizing peak and average power consumption. Then, the problem of power consumption (peak and average) in synchronous sequential designs is addressed. A solution integrating basic retiming and multiple-voltage scheduling (MVS) is proposed and evaluated. A two-stage algorithm namely power-oriented retiming followed by a MVS technique for peak and/or average power optimization is presented. Memory optimization is addressed next. Dynamic memory usage optimization during the evaluation of a special class of interdependent large data arrays is considered. Finally, this dissertation develops a novel integer-linear programming (ILP) formulation for static memory optimization using the well-known fusion technique by encoding of legality rules for loop fusion of a special class of loops using logical constraints over binary decision variables and a highly effective approximation of memory usage