Hot Chips 31 Live Blogs: Intel Spring Crest NNP-T on 16nm TSMC

Tuesday, August 20th, 2019 - Machine Learning, Teknologi

Hot Chips 31 Live Blogs: Intel Spring Crest NNP-T on 16nm TSMC


08:23PM EDT – Intel is showing us some of the design features of its new ML training product, Spring Crest.

08:23PM EDT – NNP-T = Training

08:24PM EDT – Spring Crest is what Intel purchased when it acquired Nervana in 2016. THis is the big chip that came with the acquisition

08:24PM EDT – Trend in Neural Networks means compute requirements doubles every 3.5x months

08:25PM EDT – Need to fill the die with as much compute that can be fed

08:25PM EDT – DL is as much as a communication problem as it is a compute problem

08:25PM EDT – Need a scale-out model for larger models

08:26PM EDT – Want to train a model as fast as possible within a power budget. Aim for high utilization, and a scalable solution

08:26PM EDT – Balance between compute, comms, and memory

08:26PM EDT – Best is to be compute bound on all but the smallest problems

08:27PM EDT – Keep data local and reuse it as much as possible

08:27PM EDT – Consistent programming model

08:27PM EDT – Flexibility for future workloads

08:27PM EDT – Spring Crest uses 2.5D

08:27PM EDT – PCIe Gen 4 x16 with host CPU

08:28PM EDT – 4x 8GB HBM2

08:28PM EDT – 24 Tensor Processors (TPCs), Up to 119 TOPs

08:28PM EDT – 8×8 lanes SerDes for chip-to-chip communications

08:28PM EDT – Built on 16FF+ TSMC with CoWoS

08:29PM EDT – 680mm2 with 1200mm2 passive interposer, 27 billion transistors

08:29PM EDT – Up to 1.1 GHz Core frequ

08:29PM EDT – HBM2-2400

08:29PM EDT – Supports PCIe and OAM (Open Compute)

08:29PM EDT – TensorFlow and PaddlePaddle first frameworks supported, more to come. Uses NGraph

08:30PM EDT – Intel provide the low level compiler performance optimizations

08:30PM EDT – Tensor based ISA

08:30PM EDT – Limited instruction set

08:30PM EDT – Extensible with custom microcontroller custom instructions

08:30PM EDT – Same distributed model on-chip and off-chip

08:30PM EDT – Compute has affinity for local data

08:31PM EDT – DL worklaods are dominated by a limited set of operations

08:31PM EDT – Explicit SW memory management and message passing

08:31PM EDT – 150-250W power

08:32PM EDT – Here’s a TPC

08:32PM EDT – On-chip router, controller, two arrays, memory

08:33PM EDT – Each 32×32 array has pre-op and post-op support

08:33PM EDT – dedicated convolution engine for non-MAC compute

08:33PM EDT – BFloat16 support with FP32 accumulation

08:34PM EDT – BF16 32×32 MAC Core

08:34PM EDT – 2x Multiply Cores per TPC to amortize SoC resources

08:35PM EDT – Compound vector pipeline with DL specific optimizations on non-GEMM ops

08:35PM EDT – 1.22 TBps raw HBM2 bandwidth

08:36PM EDT – 2.5MB / TPC local scratchpad memory

08:36PM EDT – Native Tensor Transpose without any overhead

08:36PM EDT – 1.4 TBps local read/write bw per TPC

08:36PM EDT – Cna do TPC-to-TPC data movement without HBM involvement

08:37PM EDT – 2D Meshes, multiple meshes for different data types

08:37PM EDT – prioritized for throughput over latency

08:37PM EDT – 1.3 TBps bandwidth in each direction

08:38PM EDT – Designed for a fully connected topology

08:38PM EDT – Looks like one large system to simplify the software model

08:38PM EDT – Up to 1024 nodes supported gluelessly

08:38PM EDT – 3.58 TBps total bidirectional SerDes BW per chip

08:39PM EDT – Fully programable router with multi-cast support and virtual channel support

08:39PM EDT – Aiming for high utilization across many GEMM sizes

08:40PM EDT – Most architectures do well on large square GEMMs. Not all hardware can do different matrix sizes well

08:40PM EDT – Looking at GEMM utilization that is difficult to solve

08:42PM EDT – Ring topology bandwidth benchmarked across 32-chips

08:42PM EDT – Equivalent bandwidth between cards and between racks

08:43PM EDT – Performance measured using 22 TPCs at 900 MHz core clock and 2 GHz HBM. Host is Xeon Gold 6130T @ 2.1 GHz

08:43PM EDT – Whisper connectivity

08:45PM EDT – Latency card-to-card at 3-9 microseconds, cross chassis at 30-36 microseconds

08:46PM EDT – Coming to customers soon

08:46PM EDT – Q&A

08:47PM EDT – Q: How do you support structured sparsity? A: More benchmarks to come

08:47PM EDT – Q: MLperf? A: Can’t comment. More data before the end of the year

08:49PM EDT – That’s a wrap. Next up Cerebras

Source link : Hot Chips 31 Live Blogs: Intel Spring Crest NNP-T on 16nm TSMC


Pictures gallery of Hot Chips 31 Live Blogs: Intel Spring Crest NNP-T on 16nm TSMC

Hot Chips 31 Live Blogs: Intel Spring Crest NNP-T on 16nm TSMC | admin | 4.5