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Evolution, Challenges, and Optimization in Computer Architecture: The Role of Reconfigurable Systems

Jefferson Ederhion, Festus Zindozin, Hillary Owusu, Chukwurimazu Ozoemezim, Mmeri Okere, Opeyemi Owolabi, Olalekan Fagbo, Oyetubo Oluwatosin

TL;DR

The paper analyzes the transition from single-core designs to multicore and domain-specific accelerators in the face of the power wall and memory bottlenecks post-Moore's Law. It surveys DSAs such as TPUs and their sparse variants (Sparse-TPU, FlexTPU) for dense and sparse workloads, and introduces RipTide as a programmable, energy-efficient dataflow architecture with a CGRA and a co-designed compiler. It also highlights Catapult, a datacenter fabric that uses FPGA-based reconfigurable hardware to achieve high throughput and reduced tail latency. The work compares architectural choices across parallelism forms and computing models, emphasizing reconfigurable systems for balancing energy efficiency, performance, and flexibility in cloud-scale workloads. Overall, the findings advocate for flexible, domain-specific, and reconfigurable architectures as practical mechanisms to sustain computational efficiency in modern and future data-centric computing.

Abstract

The evolution of computer architecture has led to a paradigm shift from traditional single-core processors to multi-core and domain-specific architectures that address the increasing demands of modern computational workloads. This paper provides a comprehensive study of this evolution, highlighting the challenges and key advancements in the transition from single-core to multi-core processors. It also examines state-of-the-art hardware accelerators, including Tensor Processing Units (TPUs) and their derivatives, RipTide and the Catapult fabric, and evaluates their strategies for optimizing critical performance metrics such as energy consumption, latency, and flexibility. Ultimately, this study emphasizes the role of reconfigurable systems in overcoming current architectural challenges and driving future advancements in computational efficiency.

Evolution, Challenges, and Optimization in Computer Architecture: The Role of Reconfigurable Systems

TL;DR

The paper analyzes the transition from single-core designs to multicore and domain-specific accelerators in the face of the power wall and memory bottlenecks post-Moore's Law. It surveys DSAs such as TPUs and their sparse variants (Sparse-TPU, FlexTPU) for dense and sparse workloads, and introduces RipTide as a programmable, energy-efficient dataflow architecture with a CGRA and a co-designed compiler. It also highlights Catapult, a datacenter fabric that uses FPGA-based reconfigurable hardware to achieve high throughput and reduced tail latency. The work compares architectural choices across parallelism forms and computing models, emphasizing reconfigurable systems for balancing energy efficiency, performance, and flexibility in cloud-scale workloads. Overall, the findings advocate for flexible, domain-specific, and reconfigurable architectures as practical mechanisms to sustain computational efficiency in modern and future data-centric computing.

Abstract

The evolution of computer architecture has led to a paradigm shift from traditional single-core processors to multi-core and domain-specific architectures that address the increasing demands of modern computational workloads. This paper provides a comprehensive study of this evolution, highlighting the challenges and key advancements in the transition from single-core to multi-core processors. It also examines state-of-the-art hardware accelerators, including Tensor Processing Units (TPUs) and their derivatives, RipTide and the Catapult fabric, and evaluates their strategies for optimizing critical performance metrics such as energy consumption, latency, and flexibility. Ultimately, this study emphasizes the role of reconfigurable systems in overcoming current architectural challenges and driving future advancements in computational efficiency.
Paper Structure (24 sections, 1 equation, 10 figures, 1 table)

This paper contains 24 sections, 1 equation, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Evolution of computer architecture
  3. Moore's Law, Dennard Scaling and the Bottleneck
  4. The Multicore Solution and Its Challenges
  5. Beyond Multicore: Heterogeneous Computing and Domain-Specific Architectures
  6. Types of Parallelism in Computer Architecture
  7. Instruction Level Parallelism (ILP)
  8. Data Level Parallelism (DLP)
  9. Thread Level Parallelism (TLP)
  10. Computing Models
  11. Von Neumann Model
  12. DataFlow Model
  13. Hybrid
  14. Choosing the Right Architecture for Domain-Specific Accelerators
  15. Tensor Processing Unit
  16. ...and 9 more sections

Figures (10)

  • Figure 1: Trends in Microprocessor Technology from 1970 to 2020. Original data up to the year 2010 collected and plotted by M. Horowitz, F. Labonte, O. Shacham, K. Olukotun, L. Hammond, and C. Batten. New plot and data collected for 2010-2019 by K. Rupp
  • Figure 2: Instruction Pipelining hennessy
  • Figure 3: Demonstration of DLP. (a) Sample 'for loop' written in C. (b) Assembly code generated by the GCC compiler, highlighting the use of SIMD instructions via Intel's Streaming SIMD Extensions (SSE), which efficiently parallelizes data processing.
  • Figure 4: Von Neumann Computing Model
  • Figure 5: A basic dataflow graph using an adder tree configuration onur
  • ...and 5 more figures