Dsptools is a Chisel library that aids in writing custom signal processing accelerators. It does this by: * Giving types and helpers that allow you to express mathematical operations more directly. * Typeclasses that let you write polymorphic generators, for example an FIR filter generator that works for both real- and complex-valued filters. * Structures for packaging DSP blocks and integrating them into a rocketchip-based SoC. * Test harnesses for testing DSP circuits, as well as VIP-style drivers and monitors for DSP blocks.

The Dsptools repository has more documentation.

6.7. Dsptools Blocks

A DspBlock is the basic unit of signal processing functionality that can be integrated into an SoC. It has a AXI4-stream interface and an optional memory interface. The idea is that these DspBlocks can be easily designed, unit tested, and assembled lego-style to build complex functionality. A DspChain is one example of how to assemble DspBlocks, in which case the streaming interfaces are connected serially into a pipeline, and a bus is instatiated and connected to every block with a memory interface.

Chipyard has example designs that integrate a DspBlock to a rocketchip-based SoC as an MMIO peripheral. The custom DspBlock has a ReadQueue before it and a WriteQueue after it, which allow memory mapped access to the streaming interfaces so the rocket core can interact with the DspBlock [1]. This section will primarily focus on designing Tilelink-based peripherals. However, through the resources provided in Dsptools, one could also define an AXI4-based peripheral by following similar steps. Furthermore, the examples here are simple, but can be extended to implement more complex accelerators, for example an OFDM baseband or a spectrometer.

Block diagram showing how FIR is integrated with rocket.

For this example, we will show you how to connect a simple FIR filter created using Dsptools as an MMIO peripheral as shown in the figure above. The full code can be found in generators/chipyard/src/main/scala/example/dsptools/GenericFIR.scala. That being said, one could substitute any module with a ready valid interface in the place of the FIR and achieve the same results. As long as the read and valid signals of the module are attached to those of a corresponding DSPBlock wrapper, and that wrapper is placed in a chain with a ReadQueue and a WriteQueue, following the general outline establised by these steps will allow you to interact with that block as a memory mapped IO.

The module GenericFIR is the overall wrapper of our FIR module. This module links together a variable number of GenericFIRDirectCell submodules, each of which performs the computations for one coefficient in a FIR direct form architecture. It is important to note that both modules are type-generic, which means that they can be instantiated for any datatype T that implements Ring operations (e.g. addition, multiplication, identities).

class GenericFIR[T<:Data:Ring](genIn:T, genOut:T, coeffs: Seq[T]) extends Module {
  val io = IO(GenericFIRIO(genIn, genOut))

  // Construct a vector of genericFIRDirectCells
  val directCells = Seq.fill(coeffs.length){ Module(new GenericFIRDirectCell(genIn, genOut)).io }

  // Construct the direct FIR chain
  for ((cell, coeff) <- directCells.zip(coeffs)) {
    cell.coeff := coeff

  // Connect input to first cell
  directCells.head.in.bits.data := io.in.bits.data
  directCells.head.in.bits.carry := Ring[T].zero
  directCells.head.in.valid := io.in.valid
  io.in.ready := directCells.head.in.ready

  // Connect adjacent cells
  // Note that .tail() returns a collection that consists of all
  // elements in the inital collection minus the first one.
  // This means that we zip together directCells[0, n] and
  // directCells[1, n]. However, since zip ignores unmatched elements,
  // the resulting zip is (directCells[0], directCells[1]) ...
  // (directCells[n-1], directCells[n])
  for ((current, next) <- directCells.zip(directCells.tail)) {
    next.in.bits := current.out.bits
    next.in.valid := current.out.valid
    current.out.ready := next.in.ready

  // Connect output to last cell
  io.out.bits.data := directCells.last.out.bits.carry
  directCells.last.out.ready := io.out.ready
  io.out.valid := directCells.last.out.valid

class GenericFIRDirectCell[T<:Data:Ring](genIn: T, genOut: T) extends Module {
  val io = IO(GenericFIRCellIO(genIn, genOut))

  // Registers to delay the input and the valid to propagate with calculations
  val hasNewData = RegInit(0.U)
  val inputReg = Reg(genIn.cloneType)

  // Passthrough ready
  io.in.ready := io.out.ready

  // When a new transaction is ready on the input, we will have new data to output
  // next cycle. Take this data in
  when (io.in.fire()) {
    hasNewData := 1.U
    inputReg := io.in.bits.data

  // We should output data when our cell has new data to output and is ready to
  // recieve new data. This insures that every cell in the chain passes its data
  // on at the same time
  io.out.valid := hasNewData & io.in.fire()
  io.out.bits.data := inputReg

  // Compute carry
  // This uses the ring implementation for + and *, i.e.
  // (a * b) maps to (Ring[T].prod(a, b)) for whicever T you use
  io.out.bits.carry := inputReg * io.coeff + io.in.bits.carry

6.7.1. Creating a DspBlock

The first step in attaching the FIR filter as a MMIO peripheral is to create an abstract subclass of DspBlock the wraps around the GenericFIR module. Streaming outputs and inputs are packed and unpacked into UInt s. If there were control signals, this is where they’d go from raw IOs to memory mapped. The main steps of this process are as follows.

  1. Instantiate a GenericFIR within GenericFIRBlock.
  2. Attach the ready and valid signals from the in and out connections.
  3. Cast the module input data to the input type of GenericFIR (GenericFIRBundle) and attach.
  4. Cast the output of GenericFIR to UInt and attach to the module output.
abstract class GenericFIRBlock[D, U, EO, EI, B<:Data, T<:Data:Ring]
  genIn: T,
  genOut: T,
  coeffs: Seq[T]
)(implicit p: Parameters) extends DspBlock[D, U, EO, EI, B] {
  val streamNode = AXI4StreamIdentityNode()
  val mem = None

  lazy val module = new LazyModuleImp(this) {
    require(streamNode.in.length == 1)
    require(streamNode.out.length == 1)

    val in = streamNode.in.head._1
    val out = streamNode.out.head._1

    // instantiate generic fir
    val fir = Module(new GenericFIR(genIn, genOut, coeffs))

    // Attach ready and valid to outside interface
    in.ready := fir.io.in.ready
    fir.io.in.valid := in.valid

    fir.io.out.ready := out.ready
    out.valid := fir.io.out.valid

    // cast UInt to T
    fir.io.in.bits := in.bits.data.asTypeOf(GenericFIRBundle(genIn))

    // cast T to UInt
    out.bits.data := fir.io.out.bits.asUInt

Note that at this point the GenericFIRBlock does not have a type of memory interface specified. This abstract class can be used to create different flavors that use AXI-4, TileLink, AHB, or whatever other memory interface you like like.

6.7.3. Top Level Traits

As in the previous MMIO example, we use a cake pattern to hook up our module to our SoC.

trait CanHavePeripheryStreamingFIR extends BaseSubsystem {
  val streamingFIR = p(GenericFIRKey) match {
    case Some(params) => {
      val streamingFIR = LazyModule(new TLGenericFIRChain(
        genIn = FixedPoint(8.W, 3.BP),
        genOut = FixedPoint(8.W, 3.BP),
        coeffs = Seq(1.F(0.BP), 2.F(0.BP), 3.F(0.BP)),
        params = params))
      pbus.toVariableWidthSlave(Some("streamingFIR")) { streamingFIR.mem.get := TLFIFOFixer() }
    case None => None

Note that this is the point at which we decide the datatype for our FIR. You could create different configs that use different types for the FIR, for example a config that instantiates a complex-valued FIR filter.

6.7.4. Constructing the Top and Config

Once again following the path of the previous MMIO example, we now want to mix our traits into the system as a whole. The code is from generators/chipyard/src/main/scala/DigitalTop.scala

class DigitalTop(implicit p: Parameters) extends ChipyardSystem
  with testchipip.CanHaveTraceIO // Enables optionally adding trace IO
  with testchipip.CanHaveBackingScratchpad // Enables optionally adding a backing scratchpad
  with testchipip.CanHavePeripheryBlockDevice // Enables optionally adding the block device
  with testchipip.CanHavePeripheryTLSerial // Enables optionally adding the backing memory and serial adapter
  with sifive.blocks.devices.uart.HasPeripheryUART // Enables optionally adding the sifive UART
  with sifive.blocks.devices.gpio.HasPeripheryGPIO // Enables optionally adding the sifive GPIOs
  with sifive.blocks.devices.spi.HasPeripherySPIFlash // Enables optionally adding the sifive SPI flash controller
  with sifive.blocks.devices.spi.HasPeripherySPI // Enables optionally adding the sifive SPI port
  with icenet.CanHavePeripheryIceNIC // Enables optionally adding the IceNIC for FireSim
  with chipyard.example.CanHavePeripheryInitZero // Enables optionally adding the initzero example widget
  with chipyard.example.CanHavePeripheryGCD // Enables optionally adding the GCD example widget
  with chipyard.example.CanHavePeripheryStreamingFIR // Enables optionally adding the DSPTools FIR example widget
  with chipyard.example.CanHavePeripheryStreamingPassthrough // Enables optionally adding the DSPTools streaming-passthrough example widget
  with nvidia.blocks.dla.CanHavePeripheryNVDLA // Enables optionally having an NVDLA
  override lazy val module = new DigitalTopModule(this)

class DigitalTopModule[+L <: DigitalTop](l: L) extends ChipyardSystemModule(l)
  with testchipip.CanHaveTraceIOModuleImp
  with sifive.blocks.devices.uart.HasPeripheryUARTModuleImp
  with sifive.blocks.devices.gpio.HasPeripheryGPIOModuleImp
  with sifive.blocks.devices.spi.HasPeripherySPIFlashModuleImp
  with sifive.blocks.devices.spi.HasPeripherySPIModuleImp
  with chipyard.example.CanHavePeripheryGCDModuleImp
  with freechips.rocketchip.util.DontTouch

Finally, we create the configuration class in generators/chipyard/src/main/scala/config/RocketConfigs.scala that uses the WithFIR mixin defined in generators/chipyard/src/main/scala/example/dsptools/GenericFIR.scala.

class WithStreamingFIR extends Config((site, here, up) => {
  case GenericFIRKey => Some(GenericFIRParams(depth = 8))
class StreamingFIRRocketConfig extends Config (
  new chipyard.example.WithStreamingFIR ++                  // use top with tilelink-controlled streaming FIR
  new freechips.rocketchip.subsystem.WithNBigCores(1) ++
  new chipyard.config.AbstractConfig)

6.7.5. FIR Testing

We can now test that the FIR is working. The test program is found in tests/streaming-fir.c.

#define PASSTHROUGH_WRITE 0x2000
#define PASSTHROUGH_READ 0x2100

#define BP 3
#define BP_SCALE ((double)(1 << BP))

#include "mmio.h"

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <stdint.h>

uint64_t roundi(double x)
  if (x < 0.0) {
    return (uint64_t)(x - 0.5);
  } else {
    return (uint64_t)(x + 0.5);

int main(void)
  double test_vector[15] = {1.0, 2.0, 3.0, 4.0, 5.0, 4.0, 3.0, 2.0, 1.0, 0.5, 0.25, 0.125, 0.125};
  uint32_t num_tests = sizeof(test_vector) / sizeof(double);
  printf("Starting writing %d inputs\n", num_tests);

  for (int i = 0; i < num_tests; i++) {
    reg_write64(PASSTHROUGH_WRITE, roundi(test_vector[i] * BP_SCALE));

  printf("Done writing\n");
  uint32_t rcnt = reg_read32(PASSTHROUGH_READ_COUNT);
  printf("Write count: %d\n", reg_read32(PASSTHROUGH_WRITE_COUNT));
  printf("Read count: %d\n", rcnt);

  int failed = 0;
  if (rcnt != 0) {
    for (int i = 0; i < num_tests - 3; i++) {
      uint32_t res = reg_read32(PASSTHROUGH_READ);
      // double res = ((double)reg_read32(PASSTHROUGH_READ)) / BP_SCALE;
      double expected_double = 3*test_vector[i] + 2*test_vector[i+1] + test_vector[i+2];
      uint32_t expected = ((uint32_t)(expected_double * BP_SCALE + 0.5)) & 0xFF;
      if (res == expected) {
        printf("\n\nPass: Got %u Expected %u\n\n", res, expected);
      } else {
        failed = 1;
        printf("\n\nFail: Got %u Expected %u\n\n", res, expected);
  } else {
    failed = 1;

  if (failed) {
    printf("\n\nSome tests failed\n\n");
  } else {
    printf("\n\nAll tests passed\n\n");
  return 0;

The test feed a series of values into the fir and compares the output to a golden model of computation. The base of the module’s MMIO write region is at 0x2000 and the base of the read region is at 0x2100 by default.

Compiling this program with make produces a streaming-fir.riscv executable.

Now we can run our simulation.

cd sims/verilator
make CONFIG=StreamingFIRRocketConfig BINARY=../../tests/streaming-fir.riscv run-binary
[1]ReadQueue and WriteQueue are good illustrations of how to write a DspBlock and how they can be integrated into rocket, but in a real design a DMA engine would be preferred. ReadQueue will stall the processor if you try to read an empty queue, and WriteQueue will stall if you try to write to a full queue, which a DMA engine can more elegantly avoid. Furthermore, a DMA engine can do the work of moving data, freeing the processor to do other useful work (or sleep).