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The use of Petri nets for modeling pipelined processors
This paper discusses the use of Petri Nets for modeling and analyzing pipelined processors. Petri Nets are particularly well-suited to modeling the synchronization, buffering, resource contention and delicate timing so common in pipelined processors. Tools for simulating, animating and analyzing the behavior of the models are described. The usefulness of the tools and the analysis methods they support in evaluating the performance and analyzing the detailed timing of pipelined microprocessors is illustrated through an example
Putting Instruction Sequences into Effect
An attempt is made to define the concept of execution of an instruction
sequence. It is found to be a special case of directly putting into effect of
an instruction sequence. Directly putting into effect of an instruction
sequences comprises interpretation as well as execution. Directly putting into
effect is a special case of putting into effect with other special cases
classified as indirectly putting into effect
Using ACL2 to Verify Loop Pipelining in Behavioral Synthesis
Behavioral synthesis involves compiling an Electronic System-Level (ESL)
design into its Register-Transfer Level (RTL) implementation. Loop pipelining
is one of the most critical and complex transformations employed in behavioral
synthesis. Certifying the loop pipelining algorithm is challenging because
there is a huge semantic gap between the input sequential design and the output
pipelined implementation making it infeasible to verify their equivalence with
automated sequential equivalence checking techniques. We discuss our ongoing
effort using ACL2 to certify loop pipelining transformation. The completion of
the proof is work in progress. However, some of the insights developed so far
may already be of value to the ACL2 community. In particular, we discuss the
key invariant we formalized, which is very different from that used in most
pipeline proofs. We discuss the needs for this invariant, its formalization in
ACL2, and our envisioned proof using the invariant. We also discuss some
trade-offs, challenges, and insights developed in course of the project.Comment: In Proceedings ACL2 2014, arXiv:1406.123
Generic Pipelined Processor Modeling and High Performance Cycle-Accurate Simulator Generation
Detailed modeling of processors and high performance cycle-accurate
simulators are essential for today's hardware and software design. These
problems are challenging enough by themselves and have seen many previous
research efforts. Addressing both simultaneously is even more challenging, with
many existing approaches focusing on one over another. In this paper, we
propose the Reduced Colored Petri Net (RCPN) model that has two advantages:
first, it offers a very simple and intuitive way of modeling pipelined
processors; second, it can generate high performance cycle-accurate simulators.
RCPN benefits from all the useful features of Colored Petri Nets without
suffering from their exponential growth in complexity. RCPN processor models
are very intuitive since they are a mirror image of the processor pipeline
block diagram. Furthermore, in our experiments on the generated cycle-accurate
simulators for XScale and StrongArm processor models, we achieved an order of
magnitude (~15 times) speedup over the popular SimpleScalar ARM simulator.Comment: Submitted on behalf of EDAA (http://www.edaa.com/
Automatic generation of hardware Tree Classifiers
Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and throughput. FPGAs are well-suited hardware platform for machine learning because of its re-programmability and lower power consumption. Programming using FPGAs for machine learning algorithms requires substantial engineering time and effort compared to software implementation. We propose a software assisted design flow to program FPGA for machine learning algorithms using our hardware library. The hardware library is highly parameterized and it accommodates Tree Classifiers. As of now, our library consists of the components required to implement decision trees and random forests. The whole automation is wrapped around using a python script which takes you from the first step of having a dataset and design choices to the last step of having a hardware descriptive code for the trained machine learning model
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