BeiHang Short Course, Part 5: Pandora Smart IP Generators
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1 BeiHang Short Course, Part 5: Pandora Smart IP Generators James C. Hoe Department of ECE Carnegie Mellon University Collaborator: Michael Papamichael J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s CONNECT NoC IP Generator Highly parameterized push button framework Multiple interfaces for configuring the desired NoC Bundled output synthesizable Verilog RTL documentations testbenches and scripts Public web based release in March 0 actively used by researchers 500+ unique visitors, 00+ NoCs generated User Breakdown 5% 0% Other Acade mia 5% Industry J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s
2 J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s
3 Generator as IP Powerful? Very! Easy to use? Not Really..... low level cryptic domain specific parameters complexity of integrating, using, tuning and validating an instantiated IP within an enclosing context If you went to CONNECT for an NoC right now which configuration would you ask for first? if not good enough, how to get a better one..... do you know what good enough is..... J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s5 Rethink the IP Paradigm Generator is the st centry IP Why limit to structural view of design Why not offer also.... pre knowledge about outcome & tradeoff of parameter combinations IP specific meaningful parameterizations, that is, ask how fast? instead of how many? any X where IP authors can do better than IP users Shift burdens from IP users to IP authors make knowledge and expertise reusable J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s6
4 Outline CONNECT Demo and Motivation Pandora Smart IP Paradigm Applying Pandora to Conclude J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s7 IP Ecosystem and Roles ors IP Users IP Autho Application Developers Assemble, configure and integrate multiple IPs to build larger chips Domain Experts Know the underlying algorithms and theory specific to the domain Hardware Experts Can build HW based on a set of specs or SW implementation J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s8
5 Pandora Smart IPs IP enriched with domain/hw expert knowledge to simplify IPuse (integration, tuning, validation) Bridge the gap between high level app goals+req s low level IP specific knowledge: algo+hw High Level IP View (use within application) May save work for the IP author in the long term Low Level IP View (HW implementation) J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s9 Pandora Key Principles Embodying Domain Expert Knowledge Raising the Level of Abstraction Instrumentation and Introspection Providing Supporting Collaterals J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s0 5
6 IP know thyself Embody Knowledge Carry quantitative and qualitative meta data facilitates faster, informed design space navigation Generic HW implementation characteristics how IP parameters affect impl. characteristics e.g., area, frequency, power/energy, memory size) Domain specific metrics and properties how IP parameters affect higher level domain metrics e.g., for NoC IP: saturation bandwidth, idle latency can also include high level properties e.g., for NoC IP: ordering guarantees J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s Varying Form and Precision Embody Knowledge HW Characterization Libs Design Space Mapping DesignerKnowledge Analytical formulas J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s 6
7 Don t ask what I don t know Raise Level of Abstraction Today s IP generators are richly parameterized neededfor efficiencyand design spacecoveragecoverage BUT too many low level parameters complexity Pandora goal oriented parameterization in terms of application level design goals meaningful and intuitive to the non expert end user Coordinated changes of multiple low level param s to achieve user high level design goals well known points, heuristic driven, feedback search leverage embedded characterization data J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s Speaking the Right Language Raise Level of Abstraction What is high level? depending on user objectives and levels of expertise varying forms and levels of abstraction Configuration interfaces Pick points in the design space, e.g., Tuning Interfaces Move within the design space, e.g., Set of good configurations or personalities + - Tuning knobs J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s Objective & Constraint Driven Queries Fuzzy trade off selectors 7
8 Do you know what your IP is doing? Instrumentations Instrumentation & Introspection tap into design for monitoring validation purposes monitor, collect and analyze info about IP s operation Introspection process low level data to reach high level conclusions diagnose & fix performance or correctness issues J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s5 Built in and Context Appropriate Instrumentation & Introspection Created by the IP author with design familiarity easy to use, no guess work by IP user Feedback interpreted with IP author knowledge natural & meaningful to IP user e.g., protocol checker and deadlock detector Greatly facilitate debug and validate Synthesizable? monitor deployed or under test designs minimal speed overhead, full fidelity capture rare or hard to reproduce behaviors J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s6 8
9 Providing Full Service Supporting Collaterals Elementary supporting material e.g., testbenches, documentation, etc. Scripts and interfaces, e.g., for configuring the IP, or processing output logs J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s7 Minimizes barrier to entry for IP user Outline CONNECT Demo and Motivation Pandora Smart IP Paradigm Applying Pandora to Conclude J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s8 9
10 Generic HW Characterization Embody Knowledge HW resource usage (e.g., slices, BRAM, LUTRAM) & frequency At the network level, router level and subcomponents Sample characterization targeting FPGAs E.g., across different router choices E.g., across different topology choices J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s9 Domain specific characterization Embody Knowledge Network performance metrics, e.g., load latency acrossvarious NoCs and traffic patterns Capture Network properties qualitative criteria and heuristics, e.g., traffic isolation, QoS properties, buffer size based on packet size Uniform Random Traffic 90% Neighbor Traffic Latency (in cycles) 0 0 J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s Load (in flits/cycle) Latency (in cycles) Load (in flits/cycle) 0
11 Interface at Multiple Expertise Levels Raise Level of Abstraction User Expertise User Input Resulting NoC Novice Intermediate Expert app personalities common configs traces comm. graph HW impl. trade off design space queries low level config. spec Pandora configuration and refinement engine J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s Novice / Intermediate / Expert Raise Level of Abstraction Pick NoC based on quantitative input, e.g., weighted graph representing communication pattern traffic traces from application runs Pick NoC based on qualitative input, e.g., mostly neighbor request response traffic, hot spots, need traffic isolation Design space query on objectives & constraints, e.g., lowest area 800MHz & has bisection bandwidth of 8 Gbps J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s These examples don t help me if my IP is not NoC :(
12 Runtime Monitoring and Analysis Instrumentation & Introspection Monitoring of events & gathering of statistics e.g., link utilization, allocation conflicts, average load per endpoint/router/port, avg. buffer occupancy, how frequently high priority traffic blocked low priority traffic Capturing sequence of events e.g., protocol ltransitions triggered ti dby flow control messages or transient temporal network behavior Built into the NoC and tailored to specific instance J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s Runtime Monitoring and Analysis Instrumentation & Introspection Dynamically detect configuration & usage issues e.g., invalid routing configuration, packets making U turns, allocation errors, improper implementation of flow control Diagnostic tools that capture domain specific effects e.g., deadlock, livelock or starvation detection offline post processing for more sophisticated analysis, e.g., detect suboptimal endpoint mapping on NoC Aid debugging, provide feedback, and guide tuning J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s
13 Generator as IP to IP as a Service Supporting Collaterals Packaging and releasing the IP as a service only need internet and browser to use CONNECT no worry about equipment, environment and tools setup transparent updates and fixes usage statistics, crowd sourcing J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s5 Recap the Last Hour Embodying Domain Knowledge Design space characterization w.r.t. HW cost + domain metrics Raise the Level of Abstraction High Level Interfaces tailored to domain, meaningful to IP user Instrumentation and Introspection Monitor, collect and analyze info about IP operation Providing Supporting Collaterals Supporting material, toolset, release strategies High Level IP RTL Introspection Uncore J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s6
14 What else could we do? Standard interfacing schema for composing IPs Can composed Smart IPs cross tune amongst themselves? Frameworks for building Smart IPs: expressing structural and meta knowledge/expertise What else to encapsulate? J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s7 Computer Architecture Lab (CALCM) Carnegie Mellon University J. C. Hoe, CMU/ECE/CALCM, 0, BHSC L5 s8
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