ASIC Clouds: Specializing the Datacenter

Transcription

1 ASIC Clouds: Specializing the Datacenter Ikuo Magaki, Moein Khazraee, Luis Vega Gutierrez, and Michael Bedford Taylor UC San Diego and Toshiba Presented By: Vandit Agarwal

2 Motivation GPU and FPGA based clouds already successful Even ASIC Clouds have been successfully used Take this idea ahead to form ASIC based clouds for other applications Purpose built Datacenter Large arrays of ASIC accelerators Optimize Total Cost of Ownership (TCO) For increasingly common high-volume chronic computations Downside: High Non Recurring Engineering (NRE) Inflexibility

3 Introduction Two visible trends: Heavy work done on cloud; interactive moved to client Rise of dark silicon - specialization and near threshold computation Conjunction of these two designs proved viable On a single machine level, ASICs can offer at least an order improvement - explore and propose ASIC cloud Identify key issues by studying Bitcoin ASIC Cloud

4 Objective In a Nutshell Two key metrics drive the development: H/w cost per performance = $ per op/s Energy per operation = W per op/s Working with a joint knowledge/control over datacenter and h/ w design Select single TCO-optimal point amongst many Paretooptimal points

5 Specialization Hierarchy Off-PCB Interface On-PCB Network On-ASIC Interconnection Network ASIC Design: achieves reduction in silicon area and energy consumption ASIC Server: organization of ASIC, heat sinks, selective components, custom voltages ASIC Datacenter: optimize rack and datacenter level thermal distribution, costs such as provisioning cost, availability, taxes etc. **To meet the requirements at datacenter level, modifications trickle down in the hierarchy

6 ASIC Cloud Architecture Off-PCB Interface On-PCB Network On-ASIC Interconnection Network Trying to create a generic skeleton for ASIC Cloud Heart of ASIC cloud - Replicated Compute Accelerator (RCA) - multiplied recursively Customization: eg - if RCA requires DRAM, then ASIC contains shared DRAM controllers connected to ASIC-local DRAMs

7 ASIC Server Overview Focussed on 1U 19-inch Rackmount servers Forced air-cooling system Air intake from front, removal from back Air at 30 o C

8 ASIC Server Evaluation Flow Given an implementation and architecture for target RCA: VLSI tools used to map it to target process Analysis tools provide info on: Area Performance Power density Tune the following to find lowest TCO: No. of RCAs/Chip No. of chips/pcb Organization of chips on PCB Power delivery mechanism Cooling mechanism Choice of voltage

9 Thermally-Aware ASIC Server Design ASICs and DC/DC convertors - major sources of heat Heat Sinks: Heat spreader glued to the heat source (die) using Thermal Interface Material (TIM) Spreader has fins - air blowed through them Increasing spreader size improves cooling Increasing the die size improves cooling - overcomes TIM resistance Developed a model: Input: fan curve, ASIC count/row Output: Optimal heat sink parameters

10 Arranging ASICs on PCB

11 More Chips vs Fewer Chips How large (in mm 2 ) should each chip be? Determines how many RCAs will be on each chip Many small ASICs easier to cool than few large ASICs Increasing silicon area -> heat dissipation capacity increases (TIM) Large total die area in a row is effective Increasing no. of chips increases the packaging cost but not by much

12 Power Density and Server Cost Given same RCA, increasing Watts, increases performance Moving right (high power density), very little total silicon per lane (due to temperature constraints) and must be divided into many smaller chips Cooling and packaging cost Moving left (low power density), more silicon per lane and fewer chips Silicon area cost

13 Bitcoin Semi-anonymously and securely transfer money Blockchain - globally replicated public ledger of transactions A distributed consensus algorithm called Byzantine Fault Tolerance determines whose transactions are added to the blockchain Mining: Machines request work from a pool server Hash - brute force attempt at partial inversion of cryptographically hard hash function Hashrate - rate of hash - typically Giga hashes per second (GH/s) On success, other machines verify. Accept and append the block

14 What Led to Bitcoin ASIC Cloud? People are incentivized to mine: More number of machine = more secure system Blockchain reward (25 BTC = ~USD 11k in 2016) 144 blocks daily x 25 BTC per block = ~USD 1.5M daily Rising TCO justifies the increased investment in NRE and other development cost Leads to more specialization

15 Bitcoin ASIC Trend Difficulty

16 Implementation 0.66 mm 2 silicon in UMC 28-nm process. Power density: 2W/mm 2 Extremely high power density

17 Results More silicon -> optimal voltages decreases -> server efficiency increases Initially, costs reduce (right to left) but then silicon costs start building up

18 Voltage Stacking DC/DC power is significant Chips serially chained so that their supplies sum to 12V Lead to significant savings in TCO optimal case

19 Litecoin ASIC Cloud

20 Video Transcoding ASIC Cloud **Pareto points are glitchy because of variations in constants and polynomial order for server components as they vary with voltages

21 CNN ASIC Cloud

22 When is ASIC Cloud Feasible

23 Discussion This is one of the earlier attempts to create a general framework/skeleton for an ASIC cloud. How feasible do you think this technology is and how widely and how soon can we potentially adopt it for a large variety of applications? The authors recommend that open sourcing various tools by the cloud providers and silicon foundries would potentially lead to lower TCO. Is this a good solution? Why or why not? What do you think is more optimal? Investing heavily in (high NRE) in more advanced nodes (eg 16nm) or using/modifying older nodes (eg 65nm) in an ASIC?

24 Bitcoin ASIC Cloud Design Repeatedly execute a Bitcoin hash operation Input: 512 bit block Mutate the block and perform SHA256 on it Fed into another round of SHA256 Leading zero count performed and matched with the target 64 rounds in each SHA

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