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[[File:Habana GOYA.png|right|thumb|Goya Logo]] | [[File:Habana GOYA.png|right|thumb|Goya Logo]] | ||
'''Goya''' is a [[16-nanometer]] microarchitecture for inference [[neural processors]] designed by [[Habana Labs]]. | '''Goya''' is a [[16-nanometer]] microarchitecture for inference [[neural processors]] designed by [[Habana Labs]]. | ||
| + | |||
| + | == Process Technology == | ||
| + | Goya-based processors are fabricated on [[TSMC]] [[16-nanometer]] process. | ||
| + | |||
| + | ==Architecture == | ||
| + | === Block Diagram === | ||
| + | :[[File:habana goya block diagram.svg|400px]] | ||
| + | |||
| + | == Overview == | ||
| + | Goya is designed as a microarchitecture for the [[acceleration]] of inference. Since the target market is the data center, the [[thermal design point]] for those chips was relatively high - at around 200 W. Goya relies on [[PCIe]] 4.0 to interface to a host processor. Habana's software compiles the models and associated instructions into independent recipes which can then be sent to the accelerator for execution. The design itself uses a heterogenous approach comprising of a large General Matrix Multiply (GMM) engine, Tensor Processor Cores (TPCs), and a large shared memory pool. | ||
| + | |||
| + | === Tensor Processor Cores (TPC) === | ||
| + | [[File:habana hl-100.jpg|right|thumb|{{habana|HL|HL-100/102}} PCIe Card]] | ||
| + | There are eight TPCs. Each TPC also incorporates its own local memory but omits caches. 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 [[data movement|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 TPCs are designed for flexibility and can be programmed in plain [[C]]. 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. | ||
| + | |||
| + | == Bibliography == | ||
| + | * {{bib|hc|31|Habana}} | ||
| + | * Habana, AI Hardware Summit 2019 | ||
| + | * Habana, Linley Fall Processor Conference 2019 | ||
| + | |||
| + | == See also == | ||
| + | * {{\\|Gaudi}} | ||
| + | * {{habana|HL}} series | ||
Latest revision as of 15:29, 28 December 2019
| Edit Values | |
| Goya µarch | |
| General Info | |
| Arch Type | NPU |
| Designer | Habana |
| Manufacturer | TSMC |
| Introduction | 2018 |
| Process | 16 nm |
| PE Configs | 8 |
| Contemporary | |
| Gaudi | |
Goya is a 16-nanometer microarchitecture for inference neural processors designed by Habana Labs.
Contents
Process Technology[edit]
Goya-based processors are fabricated on TSMC 16-nanometer process.
Architecture[edit]
Block Diagram[edit]
Overview[edit]
Goya is designed as a microarchitecture for the acceleration of inference. Since the target market is the data center, the thermal design point for those chips was relatively high - at around 200 W. Goya relies on PCIe 4.0 to interface to a host processor. Habana's software compiles the models and associated instructions into independent recipes which can then be sent to the accelerator for execution. The design itself uses a heterogenous approach comprising of a large General Matrix Multiply (GMM) engine, Tensor Processor Cores (TPCs), and a large shared memory pool.
Tensor Processor Cores (TPC)[edit]
There are eight TPCs. Each TPC also incorporates its own local memory but omits caches. 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 TPCs are designed for flexibility and can be programmed in plain C. 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.
Bibliography[edit]
- Habana, IEEE Hot Chips 31 Symposium (HCS) 2019.
- Habana, AI Hardware Summit 2019
- Habana, Linley Fall Processor Conference 2019
See also[edit]
| codename | Goya + |
| designer | Habana + |
| first launched | 2018 + |
| full page name | habana/microarchitectures/goya + |
| instance of | microarchitecture + |
| manufacturer | TSMC + |
| name | Goya + |
| process | 16 nm (0.016 μm, 1.6e-5 mm) + |
| processing element count | 8 + |