This thesis presents a code generation tool to improve the programmability of systolic array processors such as the Domain Adaptive Processor (DAP) that was designed by researchers at the University of Michigan for wireless communication workloads. Unlike application-specific integrated circuits, DAP aims to achieve high performance without trading off much on programmability and reconfigurability. The structure of a typical DAP code for each Processing Element (PE) is very different from any other programming language format. As a result, writing code for DAP requires the programmer to acquire processor-specific knowledge including configuration rules, cycle accurate execution state for memory and datapath components within each PE, etc. Each code must be carefully handcrafted to meet the strict timing and resource constraints, leading to very long programming times and low productivity. In this thesis, a code generation and optimization tool is introduced to improve the programmability of DAP and make code development easier. The tool consists of a configuration code generator, optimizer, and a scheduler. An Instruction Set Architecture (ISA) has been designed specifically for DAP. The programmer writes the assembly code for each PE using the DAP ISA. The assembly code is then translated into a low-level configuration code. This configuration code undergoes several optimizations passes. Level 1 (L1) optimization handles instruction redundancy and performs loop optimizations through code movement. The Level 2 (L2) optimization performs instruction-level parallelism. Use of L1 and L2 optimization passes result in a code that has fewer instructions and requires fewer cycles. In addition, a scheduling tool has been introduced which performs final timing adjustments on the code to match the input data rate.

This Master’s thesis includes the design, integration on-chip, and evaluation of a set of imitation learning (IL)-based scheduling policies: deep neural network (DNN)and decision tree (DT). We first developed IL-based scheduling policies for heterogeneous systems-on-chips (SoCs). Then, we tested these policies using a system-level domain-specific system-on-chip simulation framework [11]. Finally, we transformed them into efficient code using a cloud engine [1] and implemented on a user-space emulation framework [61] on a Unix-based SoC. IL is one area of machine learning (ML) and a useful method to train artificial intelligence (AI) models by imitating the decisions of an expert or Oracle that knows the optimal solution. This thesis's primary focus is to adapt an ML model to work on-chip and optimize the resource allocation for a set of domain-specific wireless and radar systems applications. Evaluation results with four streaming applications from wireless communications and radar domains show how the proposed IL-based scheduler approximates an offline Oracle expert with more than 97% accuracy and 1.20× faster execution time. The models have been implemented as an add-on, making it easy to port to other SoCs.