Hybrid AI-HPC Approaches for Scientific Discovery

 

1) Concept overview (one-sentence)

A resilient HPC cluster + AI-accelerator fabric that routes data and compute dynamically (hybrid AI–HPC) under a Vedic-inspired control plane: compute as Agni (fire), data/knowledge as Sarasvatī (flow), preservation/coordination as Vishnu, and energy/activation as Śakti — combined positively to accelerate scientific discovery workflows (simulation ↔ ML training ↔ inference ↔ analysis).

2) High-level block diagram (system “circuit”)

                   ┌────────────────────────────┐
                   │   Data Sources / Sensors   │
                   │ (instruments, experiments) │
                   └────────────┬───────────────┘
                                │ Ingest (ETL)
                                ▼
                ┌────────────────────────────────────────────┐
                │           Data Lake / NVMe Tier            │  <─── cold / warm storage
                └────────────┬───────────────────────────────┘
                             │ high-throughput PCIe/NVMe
                             ▼
┌────────────────────────────┬────────────────────────────┬───────────────────────────┐
│  CPU Host + Memory (HPC)   │  GPU/TPU Nodes (AI)        │ FPGA / Custom Accelerator │
│  (MPI, simulation kernels) │  (training, tensor ops)    │  (low-latency inference)  │
└──────────────┬─────────────┴───────────┬────────────────┴─────────────┬─────────────┘
               │                         │                              │
               │ Infiniband / 200/400GbE │ NVLink / PCIe Gen5            │ PCIe/Custom
               ▼                         ▼                              ▼
        ┌────────────┐           ┌───────────────┐               ┌───────────────┐
        │  Agni:     │           │ Sarasvatī:    │               │ Vishnu_CTRL:  │
        │  Compute   │◀──────────│ Data Fabric   │──────────────▶│ Scheduler /   │
        │  (CPU/GPU) │           │  (RDMA, DMA)  │               │ Fault Tolerance│
        └────────────┘           └───────────────┘               └───────────────┘
                    \                 |                    / 
                     \                |                   /
                      \               |                  /
                       \              |                 /
                        ▼             ▼                ▼
                     ┌─────────────────────────────────────┐
                     │  Shakti: Power, Thermal & Telemetry │
                     │  (PMICs, BMC, Fans, Sensors, AC/DC)  │
                     └─────────────────────────────────────┘

3) Component & module list (system-level "circuit parts")

• Ingest nodes: NVMe ingest servers with 200GbE — handle streaming experimental data.

• Data tier: NVMe SSD array + object store (S3-compatible) for checkpointing.

• HPC hosts: Multi-socket x86_64 servers, large DRAM, high-speed interconnect (InfiniBand HDR or 400GbE RoCE).

• AI accelerators: GPU (e.g., Hopper-class / H100-like) or TPU-like accelerators with NVLink or proprietary links.

• FPGA layer: Midplane-mounted FPGAs (Xilinx/Alveo / Intel Stratix) providing:

  • Preprocessing pipelines (filtering, compression, feature extraction)

  • Low-latency inference kernels for closed-loop experiments

  • Custom collective ops offload (all-reduce, gradient compression)

• Switching fabric: Programmable RDMA switch (Mellanox-style) for low-latency transfers.

• BMC / Control: Baseboard Management Controller + Kubernetes + Slurm + AI workflow manager (e.g., Ray or Kubeflow).

• Power & cooling: PMICs per node, redundant PDUs, liquid cooling options for dense racks.

• Monitoring: Prometheus + Grafana + telemetry aggregator (for anomaly detection).

4) FPGA/control-plane "circuit" (detailed module schematic)

This is the closest to an electronic circuit. The FPGA acts as the hybrid router/accelerator.

Functional blocks inside FPGA:

• PCIe Gen4/Gen5 Endpoint (host link)

• RDMA Offload Engine (handles RDMA requests)

• Stream Preprocessor (fixed-point DSP pipelines)

• Tensor MAC Array (FP16/INT8 MACs, pipelined)

• Ring Network Interface (connects to other FPGAs/nodes)

• Microcontroller (lightweight RISC-V softcore for control)

• DMA Controller (to/from host memory)

• Health & Telemetry block

ASCII mini-schematic (logical):

[PCIe] --> [DMA Controller] -->+--> [Stream Preprocessor] --> [Tensor MAC Array] --> [Output DMA] --> [Host Memory]
                               |
                               +--> [RDMA Offload Engine] <--> [Ring Network Interface] <--> [Other FPGAs]
                               |
                               +--> [Micro RISC-V Control Core] --> [Telemetry / PMIC I²C]

Signals and flows:

• High-throughput data lanes (PCIe) feed preprocessors.

• Control plane runs on RISC-V softcore; sends config to preprocessors and MAC arrays.

• RDMA engine surfaces direct device-to-device transfers for gradient/all-reduce.

• Telemetry uses I²C/SMBus to PMICs and sensors.

5) Mapping to Vedic archetypes (positive combination)

• Agni (Fire) = Compute cores (GPU/CPU): intense transformation — heavy simulation & training.

• Sarasvatī (Flow/Knowledge) = Data fabric: organized, flowing, enabling knowledge transfer.

• Vishnu (Preserver/Coordinator) = Scheduler / Checkpointer / Fault-tolerance: maintains system state, orchestrates.

• Śakti (Power & Activation) = PMIC / Power Fabric: supplies and gates energy; responsible for safe activation.

• Brahman (Unified field) = Policy / Governance layer: provenance, metadata, and experiment reproducibility.

Use these names as identifiers in your orchestration UI and logs — it creates an intuitive mapping and a “positive” mnemonic for operators and scientists.

6) Example usage workflows (circuits of operation)

1. Closed-loop experiment:

   • Instrument → ingest → FPGA preprocess → low-latency inference on FPGA → feedback signal to instrument (latency < 1 ms path).

2. Hybrid training:

   • Simulation runs on HPC (Agni), generates training dataset → preprocessed by FPGA (Sarasvatī) → sharded to GPUs for distributed training (all-reduce implemented partially on FPGAs to reduce network load) → checkpoints stored to NVMe.

3. Scientific discovery pipeline:

   • Large-scale simulation → surrogate model training (GPU farm) → surrogate deployed to FPGA for massive ensemble inference (fast, low power) → result aggregation & analysis.

7) Implementation notes & practical constraints

• Interconnect: Use RDMA-enabled fabric (Infiniband HDR/400GbE RoCE) for high throughput and low latency.

• Offload policy: Implement dynamic function placement: micro-controller decides whether to run preprocessing on FPGA or GPU based on queue depth and power budget.

• Precision tradeoffs: Use mixed precision for training (FP16/FP8 if supported) and INT8 for inference in FPGA.

• Power/thermal safety: Design PMIC with thermal throttling tied to Vishnu (scheduler), so jobs get paused/shifted on thermal events.

• Software stack: Linux + Slurm for HPC; Kubernetes for AI runtimes; Ray/Kubeflow for distributed ML; custom FPGA runtime exposing gRPC APIs.

• Provenance: Enforce metadata (Brahman) with UUIDs for datasets, models, and experiments so discoveries are reproducible.

8) PCB-level / circuit-schematic suggestions (if you want to go lower-level)

If you want an actual electrical schematic for the FPGA board:

• Provide: PCIe x16 edge connector, DDR4/5 for local memory, 4× QSFP DD for 200Gb links, PMIC (PMBus-enabled) for rails, temperature sensors, a CPLD for FPGA configuration sequencing, JTAG headers, and a BMC microcontroller (ARM Cortex-M).

• Power: 12V input -> onboard VRMs -> multiple synchronous buck converters. Include sequencing resistors and hot-swap circuits.

• Grounding: high-speed return paths and split analog/digital grounds where necessary.

I can produce a sample Eagle/Altium-style netlist or a single-board schematic if you’d like — tell me whether you want a system architecture diagram, a detailed FPGA RTL block (RTL pseudocode), or a PCB schematic next and I’ll generate it.

9) Quick risk & ethics note

• Carefully control access to experimental data and compute (data privacy, export controls).

• Monitor for unsafe or biased models in automated discovery workflows.

This is a design skeleton (not production-ready IP). It uses common FPGA-friendly interfaces (AXI4-Stream for high-throughput streaming, AXI4-lite for control/status, and an abstracted host PCIe endpoint interface). The comments explain where to plug vendor IP (PCIe, DDR, QSFP/RDMA) and what each module must implement next.

---

// hybrid_hpc_fpga_top.sv
// SystemVerilog skeleton for Hybrid AI-HPC FPGA layer
// Modules: PCIe endpoint (abstract), DMA, Stream Preprocessor, Tensor MAC Array,
// RDMA Offload Engine (abstract), Ring Network IF (abstract), RISC-V control core (placeholder).
// NOTE: This is skeleton RTL — replace abstract endpoints with vendor IP wrappers
// (Xilinx/Intel PCIe, DDR controller, Ethernet/RDMA cores) for synthesis.

/* ========== Top-level ========== */
module hybrid_hpc_fpga_top #(
    parameter DATA_W = 256,        // internal stream width in bits
    parameter TENSOR_W = 16,       // tensor element width (bits)
    parameter AXIL_ADDR_W = 32,
    parameter AXIL_DATA_W = 32
) (
    // Host PCIe interface (abstracted). Replace with actual PCIe IP wrapper signals.
    input  logic                    pcie_clk,
    input  logic                    pcie_rst_n,

    // AXI-Stream from host -> FPGA (e.g., through PCIe DMA engine)
    input  logic [DATA_W-1:0]       s_axis_host_tdata,
    input  logic                    s_axis_host_tvalid,
    output logic                    s_axis_host_tready,
    input  logic                    s_axis_host_tlast,

    // AXI-Stream from FPGA -> host
    output logic [DATA_W-1:0]       m_axis_host_tdata,
    output logic                    m_axis_host_tvalid,
    input  logic                    m_axis_host_tready,
    output logic                    m_axis_host_tlast,

    // AXI4-Lite control interface (from host CPU over PCIe config space / BAR)
    input  logic [AXIL_ADDR_W-1:0]  s_axil_awaddr,
    input  logic                    s_axil_awvalid,
    output logic                    s_axil_awready,
    input  logic [AXIL_DATA_W-1:0]  s_axil_wdata,
    input  logic [(AXIL_DATA_W/8)-1:0] s_axil_wstrb,
    input  logic                    s_axil_wvalid,
    output logic                    s_axil_wready,
    output logic [1:0]              s_axil_bresp,
    output logic                    s_axil_bvalid,
    input  logic                    s_axil_bready,
    input  logic [AXIL_ADDR_W-1:0]  s_axil_araddr,
    input  logic                    s_axil_arvalid,
    output logic                    s_axil_arready,
    output logic [AXIL_DATA_W-1:0]  s_axil_rdata,
    output logic [1:0]              s_axil_rresp,
    output logic                    s_axil_rvalid,
    input  logic                    s_axil_rready,

    // Ring network / RDMA links (abstractized) - connect to other FPGA nodes
    output logic [127:0]            ring_tx,
    output logic                    ring_tx_valid,
    input  logic                    ring_tx_ready,
    input  logic [127:0]            ring_rx,
    input  logic                    ring_rx_valid,
    output logic                    ring_rx_ready,

    // Onboard memory interface (e.g., DDR) - abstract
    // ... instantiate vendor DDR controller in concrete design

    // Telemetry / PMIC I2C (simple)
    output logic                    pmic_scl,
    inout  tri                      pmic_sda,

    // Misc
    output logic                    led_status
);

    // Internal wires
    logic [DATA_W-1:0]   preproc_to_mac_tdata;
    logic                preproc_to_mac_tvalid;
    logic                preproc_to_mac_tready;
    logic                preproc_to_mac_tlast;

    logic [DATA_W-1:0]   dma_to_preproc_tdata;
    logic                dma_to_preproc_tvalid;
    logic                dma_to_preproc_tready;
    logic                dma_to_preproc_tlast;

    logic [DATA_W-1:0]   mac_to_dma_tdata;
    logic                mac_to_dma_tvalid;
    logic                mac_to_dma_tready;
    logic                mac_to_dma_tlast;

    // ===== RISC-V Control Core (placeholder instantiation) =====
    // For now we expose a simple control register bus to the AXI-Lite port.
    // Replace with LiteRV or vendor softcore + debug wrapper in full design.
    logic cfg_start;
    logic cfg_reset;
    logic [31:0] cfg_status;

    axil_control #(
        .ADDR_W(AXIL_ADDR_W),
        .DATA_W(AXIL_DATA_W)
    ) u_axil_control (
        .clk(pcie_clk),
        .rst_n(pcie_rst_n),
        // AXI-Lite slave
        .s_axil_awaddr(s_axil_awaddr),
        .s_axil_awvalid(s_axil_awvalid),
        .s_axil_awready(s_axil_awready),
        .s_axil_wdata(s_axil_wdata),
        .s_axil_wstrb(s_axil_wstrb),
        .s_axil_wvalid(s_axil_wvalid),
        .s_axil_wready(s_axil_wready),
        .s_axil_bresp(s_axil_bresp),
        .s_axil_bvalid(s_axil_bvalid),
        .s_axil_bready(s_axil_bready),
        .s_axil_araddr(s_axil_araddr),
        .s_axil_arvalid(s_axil_arvalid),
        .s_axil_arready(s_axil_arready),
        .s_axil_rdata(s_axil_rdata),
        .s_axil_rresp(s_axil_rresp),
        .s_axil_rvalid(s_axil_rvalid),
        .s_axil_rready(s_axil_rready),

        // Simple control/status
        .cfg_start(cfg_start),
        .cfg_reset(cfg_reset),
        .cfg_status(cfg_status)
    );

    // ===== DMA Controller =====
    // Responsible for host memory <-> FPGA transfers. Abstracted AXI-Stream in/out interface.
    dma_controller #(
        .DATA_W(DATA_W)
    ) u_dma (
        .clk(pcie_clk),
        .rst_n(pcie_rst_n),

        // Host streaming in (from PCIe DMA engine)
        .s_axis_host_tdata(s_axis_host_tdata),
        .s_axis_host_tvalid(s_axis_host_tvalid),
        .s_axis_host_tready(s_axis_host_tready),
        .s_axis_host_tlast(s_axis_host_tlast),

        // Host streaming out
        .m_axis_host_tdata(m_axis_host_tdata),
        .m_axis_host_tvalid(m_axis_host_tvalid),
        .m_axis_host_tready(m_axis_host_tready),
        .m_axis_host_tlast(m_axis_host_tlast),

        // Local streaming interface to preproc
        .m_axis_local_tdata(dma_to_preproc_tdata),
        .m_axis_local_tvalid(dma_to_preproc_tvalid),
        .m_axis_local_tready(dma_to_preproc_tready),
        .m_axis_local_tlast(dma_to_preproc_tlast),

        .s_axis_local_tdata(mac_to_dma_tdata),
        .s_axis_local_tvalid(mac_to_dma_tvalid),
        .s_axis_local_tready(mac_to_dma_tready),
        .s_axis_local_tlast(mac_to_dma_tlast),

        // Control
        .cfg_start(cfg_start),
        .cfg_reset(cfg_reset),
        .cfg_status(cfg_status)
    );

    // ===== Stream Preprocessor =====
    // Example: normalization, filtering, simple feature extraction.
    stream_preprocessor #(
        .IN_W(DATA_W)
    ) u_preproc (
        .clk(pcie_clk), .rst_n(pcie_rst_n),

        // input stream from DMA
        .s_axis_tdata(dma_to_preproc_tdata),
        .s_axis_tvalid(dma_to_preproc_tvalid),
        .s_axis_tready(dma_to_preproc_tready),
        .s_axis_tlast(dma_to_preproc_tlast),

        // output stream to MAC
        .m_axis_tdata(preproc_to_mac_tdata),
        .m_axis_tvalid(preproc_to_mac_tvalid),
        .m_axis_tready(preproc_to_mac_tready),
        .m_axis_tlast(preproc_to_mac_tlast)
    );

    // ===== Tensor MAC Array (simple systolic / pipelined MACs) =====
    tensor_mac_array #(
        .DATA_W(DATA_W),
        .TENSOR_W(TENSOR_W)
    ) u_mac (
        .clk(pcie_clk), .rst_n(pcie_rst_n),

        .s_axis_tdata(preproc_to_mac_tdata),
        .s_axis_tvalid(preproc_to_mac_tvalid),
        .s_axis_tready(preproc_to_mac_tready),
        .s_axis_tlast(preproc_to_mac_tlast),

        .m_axis_tdata(mac_to_dma_tdata),
        .m_axis_tvalid(mac_to_dma_tvalid),
        .m_axis_tready(mac_to_dma_tready),
        .m_axis_tlast(mac_to_dma_tlast)
    );

    // ===== RDMA Offload Engine (abstract) =====
    // This module handles direct device-to-device transfers via ring network,
    // implements simple packetization for all-reduce or gradient exchange.
    rdma_offload_engine u_rdma (
        .clk(pcie_clk), .rst_n(pcie_rst_n),

        .tx_data(mac_to_dma_tdata),
        .tx_valid(mac_to_dma_tvalid),
        .tx_ready(mac_to_dma_tready),
        .tx_last(mac_to_dma_tlast),

        .ring_tx(ring_tx),
        .ring_tx_valid(ring_tx_valid),
        .ring_tx_ready(ring_tx_ready),

        .ring_rx(ring_rx),
        .ring_rx_valid(ring_rx_valid),
        .ring_rx_ready(ring_rx_ready),

        .recv_out(m_axis_host_tdata),  // as example - route received back to host stream
        .recv_valid(m_axis_host_tvalid),
        .recv_ready(m_axis_host_tready),
        .recv_last(m_axis_host_tlast)
    );

    // ===== LED status (simple heartbeat) =====
    reg [23:0] heartbeat;
    always_ff @(posedge pcie_clk or negedge pcie_rst_n) begin
        if (!pcie_rst_n) begin
            heartbeat <= 24'd0;
            led_status <= 1'b0;
        end else begin
            heartbeat <= heartbeat + 1;
            led_status <= heartbeat[23];
        end
    end

endmodule

/* ========== AXI-Lite Control Module (simple regfile) ========== */
module axil_control #(
    parameter ADDR_W = 32,
    parameter DATA_W = 32
) (
    input  logic                 clk,
    input  logic                 rst_n,
    // AXI-Lite slave signals (subset)
    input  logic [ADDR_W-1:0]    s_axil_awaddr,
    input  logic                 s_axil_awvalid,
    output logic                 s_axil_awready,
    input  logic [DATA_W-1:0]    s_axil_wdata,
    input  logic [(DATA_W/8)-1:0] s_axil_wstrb,
    input  logic                 s_axil_wvalid,
    output logic                 s_axil_wready,
    output logic [1:0]           s_axil_bresp,
    output logic                 s_axil_bvalid,
    input  logic                 s_axil_bready,
    input  logic [ADDR_W-1:0]    s_axil_araddr,
    input  logic                 s_axil_arvalid,
    output logic                 s_axil_arready,
    output logic [DATA_W-1:0]    s_axil_rdata,
    output logic [1:0]           s_axil_rresp,
    output logic                 s_axil_rvalid,
    input  logic                 s_axil_rready,

    // Simple control outputs
    output logic                 cfg_start,
    output logic                 cfg_reset,
    input  logic [31:0]          cfg_status
);
    // Simple one-register decode:
    // 0x00: CONTROL (bit0=start, bit1=reset)
    // 0x04: STATUS (read-only)
    localparam CTRL_ADDR = 32'h00;
    localparam STATUS_ADDR = 32'h04;

    // write path (very small finite-state)
    assign s_axil_awready = !s_axil_awvalid || s_axil_wvalid; // naive
    assign s_axil_wready = s_axil_awvalid; // naive coupling

    logic write_strobe;
    assign write_strobe = s_axil_awvalid && s_axil_wvalid;

    logic [DATA_W-1:0] ctrl_reg;

    always_ff @(posedge clk or negedge rst_n) begin
        if (!rst_n) begin
            ctrl_reg <= '0;
            cfg_start <= 1'b0;
            cfg_reset <= 1'b0;
        end else begin
            if (write_strobe && s_axil_awaddr == CTRL_ADDR) begin
                ctrl_reg <= s_axil_wdata;
                cfg_start <= s_axil_wdata[0];
                cfg_reset <= s_axil_wdata[1];
            end
            // self-clear start bit for edge semantics:
            if (cfg_start) cfg_start <= 1'b0;
        end
    end

    // write response
    assign s_axil_bresp = 2'b00; // OKAY
    assign s_axil_bvalid = write_strobe;

    // read path
    assign s_axil_arready = s_axil_arvalid;
    always_comb begin
        if (s_axil_arvalid) begin
            if (s_axil_araddr == CTRL_ADDR) begin
                s_axil_rdata = ctrl_reg;
            end else if (s_axil_araddr == STATUS_ADDR) begin
                s_axil_rdata = cfg_status;
            end else begin
                s_axil_rdata = '0;
            end
        end else s_axil_rdata = '0;
    end
    assign s_axil_rresp = 2'b00;
    assign s_axil_rvalid = s_axil_arvalid;

endmodule


/* ========== DMA Controller (skeleton) ========== */
module dma_controller #(
    parameter DATA_W = 256
) (
    input  logic                clk,
    input  logic                rst_n,

    // host stream in
    input  logic [DATA_W-1:0]   s_axis_host_tdata,
    input  logic                s_axis_host_tvalid,
    output logic                s_axis_host_tready,
    input  logic                s_axis_host_tlast,

    // host stream out
    output logic [DATA_W-1:0]   m_axis_host_tdata,
    output logic                m_axis_host_tvalid,
    input  logic                m_axis_host_tready,
    output logic                m_axis_host_tlast,

    // local stream (to preproc)
    output logic [DATA_W-1:0]   m_axis_local_tdata,
    output logic                m_axis_local_tvalid,
    input  logic                m_axis_local_tready,
    output logic                m_axis_local_tlast,

    // stream in from MAC (to send back to host / ring)
    input  logic [DATA_W-1:0]   s_axis_local_tdata,
    input  logic                s_axis_local_tvalid,
    output logic                s_axis_local_tready,
    input  logic                s_axis_local_tlast,

    // control
    input  logic                cfg_start,
    input  logic                cfg_reset,
    output logic [31:0]         cfg_status
);
    // Minimal flow-through logic for skeleton:
    // route host->local and local->host with simple FIFO-like buffering (abstract)
    assign s_axis_host_tready = m_axis_local_tready;
    assign m_axis_local_tdata = s_axis_host_tdata;
    assign m_axis_local_tvalid = s_axis_host_tvalid;
    assign m_axis_local_tlast  = s_axis_host_tlast;

    // route MAC/local to host out
    assign s_axis_local_tready = m_axis_host_tready;
    assign m_axis_host_tdata = s_axis_local_tdata;
    assign m_axis_host_tvalid = s_axis_local_tvalid;
    assign m_axis_host_tlast  = s_axis_local_tlast;

    // status
    assign cfg_status = {30'd0, cfg_reset, cfg_start};

endmodule


/* ========== Stream Preprocessor (parameterizable DSP chain) ========== */
module stream_preprocessor #(
    parameter IN_W = 256
) (
    input  logic                clk,
    input  logic                rst_n,
    input  logic [IN_W-1:0]     s_axis_tdata,
    input  logic                s_axis_tvalid,
    output logic                s_axis_tready,
    input  logic                s_axis_tlast,

    output logic [IN_W-1:0]    m_axis_tdata,
    output logic               m_axis_tvalid,
    input  logic               m_axis_tready,
    output logic               m_axis_tlast
);
    // Example: simple pass-through + optional normalization placeholder.
    assign s_axis_tready = m_axis_tready;
    assign m_axis_tdata = s_axis_tdata; // TODO: replace with DSP pipeline (FP/FIX arithmetic)
    assign m_axis_tvalid = s_axis_tvalid;
    assign m_axis_tlast  = s_axis_tlast;

    // Future work:
    // - Fixed-point normalization units
    // - Windowing/feature extraction (FFT, FIR)
    // - Quantization to INT8/FP16 for MAC array
endmodule


/* ========== Tensor MAC Array (high-level pipelined MACs) ========== */
module tensor_mac_array #(
    parameter DATA_W = 256,
    parameter TENSOR_W = 16
) (
    input  logic                 clk,
    input  logic                 rst_n,
    input  logic [DATA_W-1:0]    s_axis_tdata,
    input  logic                 s_axis_tvalid,
    output logic                 s_axis_tready,
    input  logic                 s_axis_tlast,

    output logic [DATA_W-1:0]   m_axis_tdata,
    output logic                m_axis_tvalid,
    input  logic                m_axis_tready,
    output logic                m_axis_tlast
);
    // For skeleton we implement a simple multiply-accumulate across lanes.
    // Suppose DATA_W is N * TENSOR_W. We perform elementwise MAC with a locally stored weight vector.

    localparam NLANES = DATA_W / TENSOR_W;

    // simple registers for weights (in practice loadable from host via AXI)
    logic [TENSOR_W-1:0] weights [0:NLANES-1];

    integer i;
    initial begin
        for (i=0;i<NLANES;i=i+1) weights[i] = '1; // default weight = 1
    end

    // naive combinational elementwise MAC -> re-packetize
    logic [DATA_W-1:0] result_packet;
    always_comb begin
        // default
        result_packet = '0;
        for (i=0;i<NLANES;i=i+1) begin
            // extract element
            logic signed [TENSOR_W-1:0] elem;
            logic signed [TENSOR_W+TENSOR_W+8-1:0] mac; // extended accumulate width
            elem = s_axis_tdata[i*TENSOR_W +: TENSOR_W];
            mac = elem * weights[i];
            // simple truncate/pack back
            result_packet[i*TENSOR_W +: TENSOR_W] = mac[TENSOR_W-1:0];
        end
    end

    assign s_axis_tready = m_axis_tready;
    assign m_axis_tdata = result_packet;
    assign m_axis_tvalid = s_axis_tvalid;
    assign m_axis_tlast = s_axis_tlast;

    // NOTE: Replace combinational MAC above with pipelined systolic array for performance.
endmodule


/* ========== RDMA Offload Engine (abstract) ========== */
module rdma_offload_engine (
    input  logic             clk,
    input  logic             rst_n,

    input  logic [255:0]     tx_data,    // local packets to send
    input  logic             tx_valid,
    output logic             tx_ready,
    input  logic             tx_last,

    output logic [127:0]     ring_tx,
    output logic             ring_tx_valid,
    input  logic             ring_tx_ready,

    input  logic [127:0]     ring_rx,
    input  logic             ring_rx_valid,
    output logic             ring_rx_ready,

    output logic [255:0]     recv_out,
    output logic             recv_valid,
    input  logic             recv_ready,
    output logic             recv_last
);
    // Abstract: packetize tx_data into ring_tx words, handshake with ring port.
    // For skeleton, we loop-back tx->rx with trivial mapping.

    assign ring_tx = tx_data[127:0];
    assign ring_tx_valid = tx_valid;
    assign tx_ready = ring_tx_ready;

    // loopback: assign incoming ring_rx to recv_out[127:0]
    assign recv_out = {128'd0, ring_rx};
    assign recv_valid = ring_rx_valid;
    assign ring_rx_ready = recv_ready;
    assign recv_last = 1'b1;

    // Real implementation:
    // - packet framing (headers: src, dst, tag, seq)
    // - reliability / retransmit or integrate over reliable fabric (e.g. RoCE)
    // - all-reduce algorithms (ring, tree, halving-doubling) for gradient aggregation
endmodule

---

What I provided

• A SystemVerilog top-level and skeleton modules for the FPGA fabric described earlier:

  • hybrid_hpc_fpga_top — top-level wiring and AXI-lite control stubs.

  • axil_control — tiny regfile mapping for start/reset/status.

  • dma_controller — abstract stream routing between host and local pipeline.

  • stream_preprocessor — placeholder pass-through; where DSP ops go.

  • tensor_mac_array — naive elementwise MAC skeleton (replace with pipelined systolic array).

  • rdma_offload_engine — abstract packetizer/loopback showing where ring/rdma logic goes.

Next recommended steps (practical)

1. Replace abstract PCIe/DDR/RDMA interfaces with your vendor IP wrappers (Xilinx/Intel IP cores).

2. Replace the combinational MAC with a pipelined systolic array or vendor DPU block for throughput.

3. Add FIFOs (Xilinx/Altera FIFO IP) and proper flow control (AXI-Stream FIFO or BRAM FIFOs) for clock domain crossing and buffering.

4. Implement host control registers for weights, quantization params, and RDMA routing tables.

5. Integrate a real RISC-V softcore (e.g., PicoRV32, VexRiscv, or RI5CY) if you want in-FPGA control — or use an on-board microcontroller/BMC to orchestrate.

6. Build unit tests / simulation: create testbench for streaming traffic, packet loss, and all-reduce correctness.

If you want I can (pick one, I’ll generate it now)

• expand tensor_mac_array into a pipelined systolic array RTL (Verilog) with testbench, or

• produce a Slurm + Kubernetes hybrid scheduling YAML + example job scripts to match this FPGA node, or

• generate a block-level PNG/SVG diagram (architectural diagram) of the FPGA + system interconnect.

I picked the RTL because it gives you hardware starting points.