Multi-Processor System-on-chip 1

Cover
Half-Title Page
Dedication
Title Page
Copyright Page
Contents
Foreword
Acknowledgments
PART 1: Processors
	1 Processors for the Internet of Things
		1.1. Introduction
		1.2. Versatile processors for low-power IoT edge devices
			1.2.1. Control processing, DSP and machine learning
			1.2.2. Configurability and extensibility
		1.3. Machine learning inference
			1.3.1. Requirements for low/mid-end machine learning inference
			1.3.2. Processor capabilities for low-power machine learning inference
			1.3.3. A software library for machine learning inference
			1.3.4. Example machine learning applications and benchmarks
		1.4. Conclusion
		1.5. References
	2 A Qualitative Approach to Many-core Architecture
		2.1. Introduction
		2.2. Motivations and context
			2.2.1. Many-core processors
			2.2.2. Machine learning inference
			2.2.3. Application requirements
		2.3. The MPPA3 many-core processor
			2.3.1. Global architecture
			2.3.2. Compute cluster
			2.3.3. VLIW core
			2.3.4. Coprocessor
		2.4. The MPPA3 software environments
			2.4.1. High-performance computing
			2.4.2. KaNN code generator
			2.4.3. High-integrity computing
		2.5. Conclusion
		2.6. References
	3 The Plural Many-core Architecture – High Performance at Low Power
		3.1. Introduction
		3.2. Related works
		3.3. Plural many-core architecture
		3.4. Plural programming model
		3.5. Plural hardware scheduler/synchronizer
		3.6. Plural networks-on-chip
			3.6.1. Scheduler NoC
			3.6.2. Shared memory NoC
		3.7. Hardware and software accelerators for the Plural architecture
		3.8. Plural system software
		3.9. Plural software development tools
		3.10. Matrix multiplication algorithm on the Plural architecture
	4 ASIP-Based Multi-Processor Systems for an Efficient Implementation of CNNs
		4.1. Introduction
		4.2. Related works
		4.3. ASIP architecture
		4.4. Single-core scaling
		4.5. MPSoC overview
		4.6. NoC parameter exploration
		4.7. Summary and conclusion
		4.8. References
PART 2: Memory
	5 Tackling the MPSoC DataLocality Challenge
		5.1. Motivation
		5.2. MPSoC target platform
		5.3. Related work
		5.4. Coherence-on-demand: region-based cache coherence
			5.4.1. RBCC versus global coherence
			5.4.2. OS extensions for coherence-on-demand
			5.4.3. Coherency region manager
			5.4.4. Experimental evaluations
			5.4.5. RBCC and data placement
		5.5. Near-memory acceleration
			5.5.1. Near-memory synchronization accelerator
			5.5.2. Near-memory queue management accelerator
			5.5.3. Near-memory graph copy accelerator
			5.5.4. Near-cache accelerator
		5.6. The big picture
		5.7. Conclusion
		5.8. Acknowledgments
		5.9. References
	6 mMPU: Building a Memristor-based General-purpose In-memory Computation Architecture
		6.1. Introduction
		6.2. MAGIC NOR gate
		6.3. In-memory algorithms for latency reduction
		6.4. Synthesis and in-memory mapping methods
			6.4.1. SIMPLE
			6.4.2. SIMPLER
		6.5. Designing the memory controller
		6.6. Conclusion
		6.7. References
	7 Removing Load/Store Helpers in Dynamic Binary Translation
		7.1. Introduction
		7.2. Emulating memory accesses
		7.3. Design of our solution
		7.4. Implementation
			7.4.1. Kernel module
			7.4.2. Dynamic binary translation
			7.4.3. Optimizing our slow path
		7.5. Evaluation
			7.5.1. QEMU emulation performance analysis
			7.5.2. Our performance overview
			7.5.3. Optimized slow path
		7.6. Related works
		7.7. Conclusion
		7.8. References
	8 Study and Comparison of Hardware Methods for Distributing Memory Bank Accesses in Many-core Architectures
		8.1. Introduction
			8.1.1. Context
			8.1.2. MPSoC architecture
			8.1.3. Interconnect
		8.2. Basics on banked memory
			8.2.1. Banked memory
			8.2.2. Memory bank conflict and granularity
			8.2.3. Efficient use of memory banks: interleaving
		8.3. Overview of software approaches
			8.3.1. Padding
			8.3.2. Static scheduling of memory accesses
			8.3.3. The need for hardware approaches
		8.4. Hardware approaches
			8.4.1. Prime modulus indexing
			8.4.2. Interleaving schemes using hash functions
		8.5. Modeling and experimenting
			8.5.1. Simulator implementation
			8.5.2. Implementation of the Kalray MPPA cluster interconnect
			8.5.3. Objectives and method
			8.5.4. Results and discussion
		8.6. Conclusion
		8.7. References
PART 3: Interconnect and Interfaces
	9 Network-on-Chip (NoC): The Technology that Enabled Multi-processor Systems-on-Chip (MPSoCs)
		9.1. History: transition from buses and crossbars to NoCs
			9.1.1. NoC architecture
			9.1.2. Extending the bus comparison to crossbars
			9.1.3. Bus, crossbar and NoC comparison summary and conclusion
		9.2. NoC configurability
			9.2.1. Human-guided design flow
			9.2.2. Physical placement awareness and NoC architecture design
		9.3. System-level services
			9.3.1. Quality-of-service (QoS) and arbitration
			9.3.2. Hardware debug and performance analysis
			9.3.3. Functional safety and security
		9.4. Hardware cache coherence
			9.4.1. NoC protocols, semantics and messaging
		9.5. Future NoC technology developments
			9.5.1. Topology synthesis and floorplan awareness
			9.5.2. Advanced resilience and functional safety for autonomous vehicles
			9.5.3. Alternatives to von Neumann architectures for SoCs
			9.5.4. Chiplets and multi-die NoC connectivity
			9.5.5. Runtime software automation
			9.5.6. Instrumentation, diagnostics and analytics for performance, safety and security
		9.6. Summary and conclusion
		9.7. References
	10 Minimum Energy Computing via Supply and Threshold Voltage Scaling
		10.1. Introduction
		10.2. Standard-cell-based memory for minimum energy computing
			10.2.1. Overview of low-voltage on-chip memories
			10.2.2. Design strategy for areaand energy-efficient SCMs
			10.2.3. Hybrid memory design towards energyand area-efficient memory systems
			10.2.4. Body biasing as an alternative to power gating
			10.2. Standard-cell-based memory for minimum energy computing
		10.3. Minimum energy point tracking
			10.3.1. Basic theory
			10.3.2. Algorithms and implementation
			10.3.3. OS-based approach to minimum energy point tracking
		10.4. Conclusion
		10.5. Acknowledgments
		10.6. References
	11 Maintaining Communication Consistency During Task Migrations in Heterogeneous Reconfigurable Devices
		11.1. Introduction
			11.1.1. Reconfigurable architectures
			11.1.2. Contribution
		11.2. Background
			11.2.1. Definitions
			11.2.2. Problem scenario and technical challenges
		11.3. Related works
			11.3.1. Hardware context switch
			11.3.2. Communication management
		11.4. Proposed communication methodology in hardware context switching
		11.5. Implementation of the communication management on reconfigurable computing architectures
			11.5.1. Reconfigurable channels in FIFO
			11.5.2. Communication infrastructure
		11.6. Experimental results
			11.6.1. Setup
			11.6.2. Experiment scenario
			11.6.3. Resource overhead
			11.6.4. Impact on the total execution time
			11.6.5. Impact on the context extract and restore time
			11.6.6. System responsiveness to context switch requests
			11.6.7. Hardware task migration between heterogeneous FPGAs
		11.7. Conclusion
		11.8. References
List of Authors
Author Biographies
Index
EULA