Editing habana/microarchitectures/gaudi

{{habana title|Gaudi|arch}}
{{microarchitecture
|atype=NPU
|name=Gaudi
|designer=Habana
|manufacturer=TSMC
|introduction=2019
|process=16 nm
|processing elements=8
|contemporary=Goya
|contemporary link=habana/microarchitectures/goya
}}
[[File:Habana GAUDI.png|right|thumb|Gaudi Logo]]
'''Gaudi''' is a [[16-nanometer]] microarchitecture for training [[neural processors]] designed by [[Habana Labs]].

== Process Technology ==
Gaudi-based processors are fabricated on [[TSMC]] [[16-nanometer]] process.

==Architecture ==
=== Block Diagram ===
:[[File:habana gaudi block diagram.svg|600px]]

== Overview ==
Gaudi was designed as a microarchitecture for the [[acceleration]] of training in the data center. It is offered as a PCIe-based [[accelerator card]] or, alternatively, as an [[OCP]] [[Open Accelerator Module]] (OAM).

The design itself is based on the company's first inference chip, {{\\|Goya}}, but adds additional components to facilitate efficient scale-out capabilities. To that end, Gaudi features eight Tensor Processing Cores (TPCs), a General Matrix Multiply (GEMM) engine, and a large pool of shared memory. In order to facilitate large scale-out capabilities, Gaudi integrates a large set of ethernet ports and [[high-bandwidth memory]].

=== General Matrix Multiply (GEMM) engine ===
Gaudi incorporates a large, shared General Matrix Multiply (GEMM) engine. The GEMM operates on 16-bit integers. Habana withheld most of the information regarding the GEMM engine. Contemporary chips such as [[Google]]'s {{google|TPU}} feature a systolic array of 128x128 in size. It's not unreasonable to expect Habana to feature a similarly-built GEMM engine. A similar architecture (128x128) at 1.2-1.5 GHz will peak at 50 [[teraFLOPS]] while a twice as large array of 256x256 at 1 GHz will peak at 131 teraFLOPS.

=== Tensor Processing Cores (TPC) ===
There are eight TPCs integrated on Gaudi, each with its own local memory and without caches. The amount of local memory has been withheld. The on-die caches and memory can be either hardware-managed or fully software-managed, allowing the compiler to optimize the residency of data and reducing movement.  Each of the individual TPCs is a VLIW DSP design that has been optimized for AI applications. This includes AI-specific instructions and operations. The design itself is actually an enhanced version of the TPCs found in the company's prior inference accelerator design, {{\\|Goya}}.

The TPC supports mixed-prevision operations including 8-bit, 16-bit, and 32-bit SIMD vector operations for both integer and floating-point. This was done in order to allow accuracy loss tolerance to be controlled on a per-model design by the programmer. Goya offers both coarse-grained precision control and fine-grained down to the tensor level. Compared to {{\\|Goya}}, the TPC in Gaudi also adds supports for [[bfloat16]] and adds additional operations and functionality more desired in training.

=== High-Bandwidth Memory (HBM2) ===
{{see also|CoWoS|High-Bandwidth Memory}}
Gaudi is fabricated on [[TSMC]] [[16 nm process]] (16FF+) and utilizes its [[2.5D]] {{tsmc|CoWoS}} interposer technology in order to integrate four stacks of [[HBM2]] memory. Each stack has 8 GiB in capacity and operates at a [[signaling rate]] of 2 GT/s for a total bandwidth of 1 TiB/s.

== Scale-Out ==
Habana made high throughput and low latency one of its key requirements. For the inter-processor communication, Gaudi relies on standard Ethernet. This was done to avoid developing its own proprietary interconnect. It also allows customers to use off-the-shelf products from an existing ecosystem. To that end, Gaudi integrates 10 ports of 100 Gigabit Ethernet (or 20x50 GbE). The chips also integrate [[RDMA over Converged Ethernet]] (RoCE) directly on-die along with the ports. The higher integration allows for the elimination of both the PCIe switch and an RDMA switch. Since this is done on per-port, the aggregated bandwidth for Gaudi is around ten times that of a solution (such as that found in the [[Nvidia V100]] that do not use {{nvidia|NVlink}}).

From a programmability point of view, Gaudi supports parameters, tensor, and sub-tensor transfers over Ethernet. For [[quality of service]], there are hardware hooks for supporting congestion control and congestion avoidance. Additionally, the fabric has both lossless and lossy support.

== Package ==
=== PCIe ===
:[[File:habana gaudi pcie.jpg|500px]]
=== OAM ===
:[[File:habana gaudi oam board.JPG|500px]]

== Bibliography ==
* {{bib|hc|31|Habana}}
* Habana, AI Hardware Summit 2019
* Habana, Linley Fall Processor Conference 2019

== See also ==
* {{\\|Goya}}
* {{habana|HL}} series
codename	Gaudi +
designer	Habana +
first launched	2019 +
full page name	habana/microarchitectures/gaudi +
instance of	microarchitecture +
manufacturer	TSMC +
name	Gaudi +
process	16 nm (0.016 μm, 1.6e-5 mm) +
processing element count	8 +