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The Saturn Microarchitecture Manual

Authors: Jerry Zhao, Daniel Grubb, Miles Rusch, Tianrui Wei, Kevin Anderson, Borivoje Nikolic, Krste Asanovic
version v1.0.0, -2024-11-08 +2024-11-12
Release
@@ -467,7 +467,7 @@

The Saturn Microarchitecture Manual

  • 1.1. Data-parallel Workloads
  • 1.2. Data-parallel Architectures
  • 1.3. The RISC-V Vector ISA
  • -
  • 1.4. Comparing Short-Vector Units
  • +
  • 1.4. Short-Vector Execution
  • 2. System Overview @@ -674,7 +674,7 @@

    1.2.1. Long-vector Implementations

    Indexed loads and stores can leverage the distributed memory access ports to maintain high memory utilization, while register-register scatters and gathers can leverage a complex yet scalable cross-lane interconnect.

  • -

    The NEC Aurora [3] is a modern example of a commercial long-vector microarchitecture, while Hwacha [10], Ara [13], and Vitruvius [11] are examples of academic long-vector implementations.

    +

    The NEC Aurora [3] is a modern example of a commercial long-vector microarchitecture, while Hwacha [11], Ara [14], and Vitruvius [12] are examples of academic long-vector implementations.

    @@ -714,7 +714,7 @@

    1.2.3. General-purpose ISAs with SIMD

    Practically all widely deployed commercial general-purpose cores ship with SIMD extensions. -Intel and AMD out-of-order cores support some form of the SSE or AVX extensions [5], while ARM’s A-profile [1] architecture requires NEON.

    +Intel and AMD out-of-order cores support some form of the SSE or AVX extensions [6], while ARM’s A-profile [1] architecture requires NEON.

    @@ -749,7 +749,7 @@

    1.2.5. Scalable Vector ISAs

    Compared to most prior academic implementations, Saturn targets DSP and domain-specialized cores, and represents a class of designs we call "short-vector".

    -

    Existing open-source "short-vector" implementations like Spatz [8], Vicuna [14], and RISCV2 [12] require either register-renaming or constricted memory systems. +

    Existing open-source "short-vector" implementations like Spatz [9], Vicuna [15], and RISCV2 [13] require either register-renaming or constricted memory systems. RISCV2 requires register-renaming, while Spatz and Vicuna assume a low-latency memory system. Vicuna further requires a global stall to adapt to variable memory latencies. Unlike these designs, Saturn demonstrates that a "short-vector" design can achieve high performance and efficiency without any architectural or microarchitectural sacrifices.

    @@ -844,7 +844,7 @@

    1.3.5. Segmented Memory Instructions

    -

    1.4. Comparing Short-Vector Units

    +

    1.4. Short-Vector Execution

    Saturn’s instruction scheduling mechanism differentiates it from the relevant comparable archetypes for data-parallel microarchitectures. Fundamentally, Saturn relies on efficient dynamic scheduling of short-chime short-vectors, without relying on costly register renaming. @@ -988,7 +988,7 @@

    1.4.3. Compared to VLIW + SIMD DSP Cor

    2. System Overview

    -

    Saturn is implemented using the Chisel hardware description language [7], and is intended to integrate into existing Chisel-based cores as part of the open-source Chipyard SoC framework. +

    Saturn is implemented using the Chisel hardware description language [8], and is intended to integrate into existing Chisel-based cores as part of the open-source Chipyard [5] SoC framework. The generator is a parameterized Scala program that uses the Chisel embedded DSL to generate a synthesizable RTL circuit given a user-defined configuration.

    @@ -1015,7 +1015,8 @@

    2.1. Organization

    The load/store paths within the VLSU execute independently and communicate with the VU through load-response and store-data ports.

    -

    The Vector Datapath (VU) contains instruction issue queues (VIQs), vector sequencers (VXS/VLS/VSS), the vector register file (VRF), and the SIMD arithmetic functional units (VEUs/VFUs). +

    The Vector Datapath (VU) contains instruction issue queues (VIQs), vector sequencers (VXS/VLS/VSS), and the vector register file (VRF), and the SIMD arithmetic functional units. +The functional units (VFUs) are arranged in execution unit clusters (VEUs), where each VEU is fed by one sequencer. The sequencers schedule register read/write and issue operations into the VEUs, while interlocking on structural and data hazards. The VU is organized as a unified structure with a SIMD datapath, instead of distributing the VRF and VEUs across vector lanes. This approach is better suited for compact designs, where scalability to ultra-wide datapaths is less of a concern.

    @@ -1041,7 +1042,7 @@

    2.2. Key Ideas

    The in-order execution of the load/store paths aligns with the in-order load-response and store-data ports.

    -

    Saturn is organized as a decoupled access-execute (DAE) [15] architecture, where the VLSU acts as the "access processor" and the VU acts as the "execute processor". +

    Saturn is organized as a decoupled access-execute (DAE) [16] architecture, where the VLSU acts as the "access processor" and the VU acts as the "execute processor". Shallow instruction queues in the VU act as "decoupling" queues, enabling the VLSU’s load-path to run many instructions ahead of the VU. Similarly, the VLSU’s store path can run many cycles behind the VU through the decoupling enabled by the VSIQ. This approach can tolerate high memory latencies with minimal hardware cost.

    @@ -1260,7 +1261,7 @@

    3.3. Scalar-Vector Memory Disambiguation

    3.4. Interface to VU and VLSU

    -

    The VectorIssueInst bundle presented to the VU and VLSU contains the instruction bits, scalar operands, and current vtype/vstart/vl settings for this instruction. +

    The micro-op presented to the VU and VLSU contains the instruction bits, scalar operands, and current vtype/vstart/vl settings for this instruction. For memory operations, this bundle also provides the physical page index of the accessed page for this instruction, since the PFC and IFC crack vector memory instructions into single-page accesses. For segmented instructions where a segment crosses a page, segstart and segend bits are additionally included in the bundle, to indicate which slice of a segment resides in the current page.

    @@ -1273,7 +1274,7 @@

    4. Vector Load-Store Unit

    -LSU +LSU
    Figure 15. Vector Load-Store Unit Diagram
    @@ -1663,7 +1664,7 @@

    5.2. Operation Sequencers

    Notably, the sequencers enact "fire-and-forget" operation issue. -Once an operation is issued by a sequencer, it is guaranteed to be free of further structural or data hazards as it proceeds down the pipelined VFU datapaths. +Once an operation is issued by a sequencer, it is guaranteed to be free of further structural or data hazards as it proceeds down the pipelined functional unit datapaths. This eliminates the need for costly operand or result queues and obviates back-pressure in the functional unit pipelines.

    @@ -1691,8 +1692,8 @@

    5.2.2. Execute Sequencer

    The execute sequencers (VXSs) sequence all arithmetic operations. They track up to three register operands, with up to four reads and one write per operation (for a masked FMA). Each VXS issues to a single vector execution unit (VXU). -A VXU is a collection of vector functional units (VFUs). -The VXSs will stall operation execution if the requested VFU within its VXU is unavailable.

    +A VXU is a collection of vector functional units. +The VXSs will stall operation execution if the requested functional unit within its VXU is unavailable.

    @@ -1756,7 +1757,7 @@

    5.3. Hazards

    In some cases, the relative age is unambiguous, so no age filter is needed. Instructions in the sequencer are inherently older than instructions from the feeding issue queue for that sequencer, so no age filter is needed. -Sequenced operations in the VFUs are inherently the oldest writes to any element group, so no age filter is needed for these either.

    +Sequenced operations in the functional units are inherently the oldest writes to any element group, so no age filter is needed for these either.

    Each sequencer computes the element groups that will be accessed or written to by the next operation to be issued, and determines if a pending older read or write to those element groups would induce a RAW, WAR or WAR hazard. @@ -2036,7 +2037,7 @@

    7.1. Prior Vector Implementations

    7.1.1. Torrent-0

    -

    T-0 [6] was an early demonstration of the principles of instruction scheduling in short-vector microarchitectures. +

    T-0 [7] was an early demonstration of the principles of instruction scheduling in short-vector microarchitectures. Torrent demonstrated a decoupled-access-execute-style microarchitecture with efficient scheduling of short-chime vector instructions. However, Torrent relied on fixed memory and pipeline latencies for instruction scheduling, relying on a global stall to handle cache misses.

    @@ -2048,7 +2049,7 @@

    7.1.1. Torrent-0

    7.1.2. Hwacha

    -

    Hwacha [10] was a decoupled-fetch vector unit designed to support scalability to many-lane designs. +

    Hwacha [11] was a decoupled-fetch vector unit designed to support scalability to many-lane designs. Microarchitecturally, Hwacha is a classic "long-vector" machine, with deeply temporal vector execution distributed across parallel independent vector lanes. Each vector lane contained an independent instruction sequencer, independent functional units, a slice of the register file, and an independent memory port. Like Saturn, Hwacha was implemented in Chisel and could integrate with Rocket as a post-commit decoupled unit.

    @@ -2071,7 +2072,7 @@

    7.1.2. Hwacha

    7.1.3. LEM

    -

    LEM [9] was an experimental microcoded RVV vector unit designed around an early variant of the Shuttle superscalar core. +

    LEM [10] was an experimental microcoded RVV vector unit designed around an early variant of the Shuttle superscalar core. Some ideas around instruction decode in LEM inspired the vector decode generators in Saturn. LEM was the first Chisel implementation of an RVV-compliant vector unit.

    @@ -2169,37 +2170,40 @@

    Bibliography

    [4] “RISC-V ‘V’ Vector Extension.” Sep. 2021, Accessed: Apr. 17, 2024. [Online]. Available: github.com/riscv/riscv-v-spec/releases/download/v1.0/riscv-v-spec-1.0.pdf.

    -

    [5] C. S. Anderson, J. Zhang, and M. Cornea, “Enhanced Vector Math Support on the IntelAVX-512 Architecture,” in 2018 IEEE 25th Symposium on Computer Arithmetic (ARITH), Amherst, MA, USA, Jun. 2018, pp. 120–124, Accessed: Apr. 18, 2024. [Online]. Available: ieeexplore.ieee.org/document/8464794/.

    +

    [5] A. Amid et al., “Chipyard: Integrated Design, Simulation, and Implementation Framework for Custom SoCs,” IEEE Micro, vol. 40, no. 4, pp. 10–21, 2020, doi: 10.1109/MM.2020.2996616.

    -

    [6] K. Asanović, “Vector Microprocessors,” PhD thesis, EECS Department, University of California, Berkeley, 1998.

    +

    [6] C. S. Anderson, J. Zhang, and M. Cornea, “Enhanced Vector Math Support on the IntelAVX-512 Architecture,” in 2018 IEEE 25th Symposium on Computer Arithmetic (ARITH), Amherst, MA, USA, Jun. 2018, pp. 120–124, Accessed: Apr. 18, 2024. [Online]. Available: ieeexplore.ieee.org/document/8464794/.

    -

    [7] J. Bachrach et al., “Chisel: Constructing Hardware in a Scala Embedded Language,” in DAC Design Automation Conference 2012, Jun. 2012, pp. 1212–1221, Accessed: Apr. 18, 2024. [Online]. Available: ieeexplore.ieee.org/document/6241660.

    +

    [7] K. Asanović, “Vector Microprocessors,” PhD thesis, EECS Department, University of California, Berkeley, 1998.

    -

    [8] M. Cavalcante, D. Wüthrich, M. Perotti, S. Riedel, and L. Benini, “Spatz: A Compact Vector Processing Unit for High-Performance and Energy-Efficient Shared-L1 Clusters,” in Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, New York, NY, USA, Dec. 2022, pp. 1–9, Accessed: Apr. 15, 2024. [Online]. Available: dl.acm.org/doi/10.1145/3508352.3549367.

    +

    [8] J. Bachrach et al., “Chisel: Constructing Hardware in a Scala Embedded Language,” in DAC Design Automation Conference 2012, Jun. 2012, pp. 1212–1221, Accessed: Apr. 18, 2024. [Online]. Available: ieeexplore.ieee.org/document/6241660.

    -

    [9] Z. Fang, “LEM: A Configurable RISC-v Vector Unit Based on Parameterized Microcode Expander,” no. EECS-2022-150, 2022.

    +

    [9] M. Cavalcante, D. Wüthrich, M. Perotti, S. Riedel, and L. Benini, “Spatz: A Compact Vector Processing Unit for High-Performance and Energy-Efficient Shared-L1 Clusters,” in Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design, New York, NY, USA, Dec. 2022, pp. 1–9, Accessed: Apr. 15, 2024. [Online]. Available: dl.acm.org/doi/10.1145/3508352.3549367.

    -

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