Variable Precision DSP Blocks User Guide: Agilex™ 5 FPGAs and SoCs

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ID 813968
Date 4/01/2024
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Document Table of Contents
Document Table of Contents x
1. Agilex™ 5 Variable Precision DSP Blocks Overview 2. Agilex™ 5 Variable Precision DSP Blocks Architecture 3. Agilex™ 5 Variable Precision DSP Blocks Operational Modes 4. Agilex™ 5 Variable Precision DSP Blocks Design Considerations 5. Native Fixed Point DSP Agilex™ FPGA IP Core References 6. Multiply Adder Intel® FPGA IP Core References 7. ALTMULT_COMPLEX Intel® FPGA IP Core References 8. LPM_MULT Intel® FPGA IP Core References 9. LPM_DIVIDE (Divider) Intel FPGA IP Core 10. Native Floating Point DSP Agilex™ FPGA IP References 11. Native AI Optimized DSP Agilex™ FPGA IP References 12. Document Revision History for the Agilex™ 5 Variable Precision DSP Blocks User Guide
1. Agilex™ 5 Variable Precision DSP Blocks Overview x
1.1. Features 1.2. Supported Operational Modes in Agilex™ 5 Devices
1.2. Supported Operational Modes in Agilex™ 5 Devices x
1.2.1. Fixed-point Arithmetic 1.2.2. Floating-point Arithmetic
2. Agilex™ 5 Variable Precision DSP Blocks Architecture x
2.1. Fixed-point Arithmetic 2.2. Floating-point Arithmetic 2.3. Tensor Mode
2.1. Fixed-point Arithmetic x
2.1.1. Input Register Bank for Fixed-point Arithmetic 2.1.2. Pipeline Registers for Fixed-point Arithmetic 2.1.3. Pre-adder for Fixed-point Arithmetic 2.1.4. Internal Coefficient for Fixed-point Arithmetic 2.1.5. Multipliers for Fixed-point Arithmetic 2.1.6. Adder or Subtractor for Fixed-point Arithmetic 2.1.7. Accumulator, Chainout Adder, and Preload Constant for Fixed-point Arithmetic 2.1.8. Systolic Register for Fixed-point Arithmetic 2.1.9. Double Accumulation Register for Fixed-point Arithmetic 2.1.10. Output Register Bank for Fixed-point Arithmetic
2.1.7. Accumulator, Chainout Adder, and Preload Constant for Fixed-point Arithmetic x
2.1.7.1. Dynamic Chainout
2.2. Floating-point Arithmetic x
2.2.1. Input Register Bank for Floating-point Arithmetic 2.2.2. Pipeline Registers for Floating-point Arithmetic 2.2.3. Multipliers for Floating-point Arithmetic 2.2.4. Adder or Subtractor for Floating-point Arithmetic 2.2.5. Output Register Bank for Floating-point Arithmetic 2.2.6. Exception Handling for Floating-point Arithmetic
2.3. Tensor Mode x
2.3.1. Input Register Bank for Tensor Mode 2.3.2. DOT Product Vector Engines for Tensor Mode 2.3.3. Pipeline Registers for Tensor Mode 2.3.4. Fixed-point to Floating-point Converter for Tensor Mode 2.3.5. Cascade Input and Datapath Registers for Tensor Mode 2.3.6. Dynamic Control Multiplexer Bank for Tensor Mode 2.3.7. Accumulator for Tensor Mode 2.3.8. Output Registers for Tensor Mode
2.3.1. Input Register Bank for Tensor Mode x
2.3.1.1. Data Input Feed Preloading Method 2.3.1.2. Side Input Feed Preloading Method
2.3.4. Fixed-point to Floating-point Converter for Tensor Mode x
2.3.4.1. Fixed-point to 32-bit Floating-point Conversion Examples 2.3.4.2. Exception Handling for Floating-point Conversion
3. Agilex™ 5 Variable Precision DSP Blocks Operational Modes x
3.1. Operational Modes for Fixed-point Arithmetic 3.2. Operational Modes for Floating-point Arithmetic 3.3. Operational Modes for Tensor Mode
3.1. Operational Modes for Fixed-point Arithmetic x
3.1.1. Independent Multiplier Mode 3.1.2. Multiplier Adder Sum Mode 3.1.3. Independent Complex Multiplier 3.1.4. Systolic FIR Mode
3.1.1. Independent Multiplier Mode x
3.1.1.1. 18 × 18 or 18 × 19 Independent Multiplier 3.1.1.2. 27 × 27 Independent Multiplier
3.1.2. Multiplier Adder Sum Mode x
3.1.2.1. 8 x 8 (Unsigned) or 9 x 9 (Signed) Sum of 6 Mode
3.1.2.1. 8 x 8 (Unsigned) or 9 x 9 (Signed) Sum of 6 Mode x
3.1.2.1.1. 18 × 19 Multiplication Summed with 36-Bit Input Mode
3.1.4. Systolic FIR Mode x
3.1.4.1. Mapping Systolic Mode User View to Variable Precision Block Architecture View 3.1.4.2. 18-bit Systolic FIR Mode 3.1.4.3. 27-Bit Systolic FIR Mode
3.2. Operational Modes for Floating-point Arithmetic x
3.2.1. FP32 Single-precision Floating-point Arithmetic Functions 3.2.2. FP16 Half-precision Floating-point Arithmetic Functions 3.2.3. Multiple Floating-point Variable DSP Blocks Functions
3.2.1. FP32 Single-precision Floating-point Arithmetic Functions x
3.2.1.1. FP32 Multiplication Mode 3.2.1.2. Adder or Subtract Mode 3.2.1.3. Multiply Accumulate Mode 3.2.1.4. FP32 Vector One Mode 3.2.1.5. FP32 Vector Two Mode
3.2.2. FP16 Half-precision Floating-point Arithmetic Functions x
3.2.2.1. FP16 Supported Precision Formats 3.2.2.2. Sum of Two FP16 Multiplication Mode 3.2.2.3. Sum of Two FP16 Multiplication with FP32 Addition Mode 3.2.2.4. Sum of Two FP16 Multiplication with Accumulation Mode 3.2.2.5. FP16 Vector One Mode 3.2.2.6. FP16 Vector Two Mode 3.2.2.7. FP16 Vector Three Mode
3.2.3. Multiple Floating-point Variable DSP Blocks Functions x
3.2.3.1. Multiply-Add or Multiply-Subtract Mode 3.2.3.2. Direct Vector Dot Product 3.2.3.3. Complex Multiplication
3.3. Operational Modes for Tensor Mode x
3.3.1. Tensor Floating-point Mode 3.3.2. Tensor Fixed-point Mode 3.3.3. Tensor Accumulation Mode
4. Agilex™ 5 Variable Precision DSP Blocks Design Considerations x
4.1. Fixed-point Arithmetic 4.2. Floating-point Arithmetic 4.3. DSP Block Cascade Limit in Agilex™ 5 Devices
4.1. Fixed-point Arithmetic x
4.1.1. Configurations for Input, Pipeline, and Output Registers 4.1.2. Internal Coefficient and Pre-Adder for Fixed-point Arithmetic 4.1.3. Accumulator for Fixed-point Arithmetic 4.1.4. Input Cascade for Fixed-point Arithmetic 4.1.5. Chainout Adder
4.1.1. Configurations for Input, Pipeline, and Output Registers x
4.1.1.1. Restrictions for Input Registers 4.1.1.2. Restrictions for Pipeline Registers 4.1.1.3. Supported Register Configurations per Operation Modes
4.1.4. Input Cascade for Fixed-point Arithmetic x
4.1.4.1. Dynamic Scanin
4.2. Floating-point Arithmetic x
4.2.1. Configurations for Input, Pipeline, and Output Registers 4.2.2. Chainout Adder
4.2.1. Configurations for Input, Pipeline, and Output Registers x
4.2.1.1. FP32 Operation Modes Supported Register Configurations 4.2.1.2. FP16 Operation Mode Supported Register Configurations
5. Native Fixed Point DSP Agilex™ FPGA IP Core References x
5.1. Native Fixed Point DSP Agilex™ FPGA IP Release Information 5.2. Supported Operational Modes 5.3. Maximum Input Data Width for Fixed-point Arithmetic 5.4. Maximum Output Data Width for Fixed-point Arithmetic 5.5. Parameterizing Native Fixed Point DSP IP 5.6. Native Fixed Point DSP Agilex™ FPGA IP Signals 5.7. IP Migration
5.3. Maximum Input Data Width for Fixed-point Arithmetic x
5.3.1. Using Less Than 36-Bit Operand In 18 x 18 Plus 36 Mode Example
5.5. Parameterizing Native Fixed Point DSP IP x
5.5.1. Operation Mode Tab 5.5.2. Input Cascade Tab 5.5.3. Pre-adder Tab 5.5.4. Internal Coefficient Tab 5.5.5. Accumulator/Output Chaining 5.5.6. Pipelining 5.5.7. Clear Signal
5.6. Native Fixed Point DSP Agilex™ FPGA IP Signals x
5.6.1. 9 × 9 Sum of 6 Mode Signals 5.6.2. 16 × 16 Complex Multiplier Mode Signals 5.6.3. 18 × 18 Full Mode Signals 5.6.4. 18 × 18 Sum of Two Mode Signals 5.6.5. 18 × 18 Plus 36 Mode Signals 5.6.6. 18 × 18 Systolic Mode Signals 5.6.7. 27 × 27 Mode Signals
6. Multiply Adder Intel® FPGA IP Core References x
6.1. Multiply Adder Intel® FPGA IP Release Information 6.2. Features 6.3. Parameters 6.4. Signals
6.2. Features x
6.2.1. Pre-adder 6.2.2. Systolic Delay Register 6.2.3. Pre-load Constant 6.2.4. Double Accumulator
6.2.1. Pre-adder x
6.2.1.1. Pre-adder Simple Mode 6.2.1.2. Pre-adder Coefficient Mode 6.2.1.3. Pre-adder Input Mode 6.2.1.4. Pre-adder Square Mode 6.2.1.5. Pre-adder Constant Mode
6.3. Parameters x
6.3.1. General Tab 6.3.2. Extra Modes 6.3.3. Multipliers Tab 6.3.4. Preadder Tab 6.3.5. Accumulator Tab 6.3.6. Systolic/Chainout Tab 6.3.7. Pipelining Tab
7. ALTMULT_COMPLEX Intel® FPGA IP Core References x
7.1. ALTMULT_COMPLEX Intel® FPGA IP Release Information 7.2. Features 7.3. Parameters 7.4. Signals
8. LPM_MULT Intel® FPGA IP Core References x
8.1. LPM_MULT Intel® FPGA IP Release Information 8.2. Features 8.3. Parameters 8.4. Signals
8.3. Parameters x
8.3.1. General Tab 8.3.2. General 2 Tab 8.3.3. Pipelining Tab
9. LPM_DIVIDE (Divider) Intel FPGA IP Core x
9.1. LPM_DIVIDE Intel® FPGA IP Release Information 9.2. Features 9.3. Verilog HDL Prototype 9.4. VHDL Component Declaration 9.5. VHDL LIBRARY_USE Declaration 9.6. Ports 9.7. Parameters
9.7. Parameters x
9.7.1. General Tab 9.7.2. General1 Tab
10. Native Floating Point DSP Agilex™ FPGA IP References x
10.1. Native Floating Point DSP Agilex™ FPGA IP Release Information 10.2. Native Floating Point DSP Agilex™ FPGA IP Core Supported Operational Modes 10.3. Parameterizing the Native Floating Point DSP Agilex™ FPGA IP 10.4. Native Floating Point DSP Agilex™ FPGA IP Core Signals 10.5. IP Migration
10.3. Parameterizing the Native Floating Point DSP Agilex™ FPGA IP x
10.3.1. General Tab 10.3.2. Registers Tab
10.4. Native Floating Point DSP Agilex™ FPGA IP Core Signals x
10.4.1. FP32 Multiplication Mode Signals 10.4.2. FP32 Addition or Subtraction Mode Signals 10.4.3. FP32 Multiplication with Addition or Subtraction Mode Signals 10.4.4. FP32 Multiplication with Accumulation Mode Signals 10.4.5. FP32 Vector One and Vector Two Modes Signals 10.4.6. Sum of Two FP16 Multiplication Mode Signals 10.4.7. Sum of Two FP16 Multiplication with FP32 Addition Mode Signals 10.4.8. Sum of Two FP16 Multiplication with Accumulation Mode Signals 10.4.9. FP16 Vector One and Vector Two Modes Signals 10.4.10. FP16 Vector Three Mode Signals
11. Native AI Optimized DSP Agilex™ FPGA IP References x
11.1. Native AI Optimized DSP Agilex™ FPGA IP Release Information 11.2. Native AI Optimized DSP Agilex™ FPGA IP Core Supported Operational Modes 11.3. Parameterizing the Native AI Optimized DSP Agilex™ FPGA IP 11.4. Native AI Optimized DSP Agilex™ FPGA IP Core Signals 11.5. IP Migration
11.3. Parameterizing the Native AI Optimized DSP Agilex™ FPGA IP x
11.3.1. Operation Mode Tab 11.3.2. Clock Source Enable/Clear Tab
11.4. Native AI Optimized DSP Agilex™ FPGA IP Core Signals x
11.4.1. Tensor Floating-point Mode Signals 11.4.2. Tensor Fixed-point Mode Signals 11.4.3. Tensor Accumulation Mode Signals
1. Agilex™ 5 Variable Precision DSP Blocks Overview
2. Agilex™ 5 Variable Precision DSP Blocks Architecture
3. Agilex™ 5 Variable Precision DSP Blocks Operational Modes
4. Agilex™ 5 Variable Precision DSP Blocks Design Considerations
5. Native Fixed Point DSP Agilex™ FPGA IP Core References
6. Multiply Adder Intel® FPGA IP Core References
7. ALTMULT_COMPLEX Intel® FPGA IP Core References
8. LPM_MULT Intel® FPGA IP Core References
9. LPM_DIVIDE (Divider) Intel FPGA IP Core
10. Native Floating Point DSP Agilex™ FPGA IP References
11. Native AI Optimized DSP Agilex™ FPGA IP References
12. Document Revision History for the Agilex™ 5 Variable Precision DSP Blocks User Guide

3.3.2. Tensor Fixed-point Mode

In tensor fixed-point mode, two columns of 80-bit weights can be preloaded to the ping-pong buffers by using one of the following methods:
  • Data input feed
  • Side input feed

The ping-pong buffers load the data to two DOT product vector engines to start calculating the signed 20-bit fixed-point DOT product simultaneously. DOT product can support 10 signed 8x8 multiplication. Next, the CPA adder adds the output of each DOT product to its respective 32-bit fixed-point values either the data input from the cascade_data_in[63:0] bus or the previous cycle’s accumulation value. The CPA adder only perform addition. The CPA adder output the data in 32-bit fixed-point format to the core fabric or to the next DSP block in the chain through the cascade_data_out[63:0] bus.

Table 27.  Tensor Fixed-point Mode Equations for Data Input Feed Method
Input Operands Cascade Input Enabled Accumulator Enabled
10-element signed 8x8

Column One = column one cascade_data_in[31:0] + 32-bit fixed-point of (data_in_1[7:0]*data_in_1[7:0] + data_in_2[7:0]*data_in_2[7:0] + data_in_3[7:0]*data_in_3[7:0] + data_in_4[7:0]*data_in_4[7:0] + data_in_5[7:0]*data_in_5[7:0] + data_in_6[7:0]*data_in_6[7:0] + data_in_7[7:0]*data_in_7[7:0] + data_in_8[7:0]*data_in_8[7:0] + data_in_9[7:0]*data_in_9[7:0] + data_in_10[7:0]*data_in_10[7:0])

Column One = column one col_one_accresult[31:0]+ 32-bit fixed-point of (data_in_1[7:0]*data_in_1[7:0] + data_in_2[7:0]*data_in_2[7:0] + data_in_3[7:0]*data_in_3[7:0] + data_in_4[7:0]*data_in_4[7:0] + data_in_5[7:0]*data_in_5[7:0] + data_in_6[7:0]*data_in_6[7:0] + data_in_7[7:0]*data_in_7[7:0] + data_in_8[7:0]*data_in_8[7:0] + data_in_9[7:0]*data_in_9[7:0] + data_in_10[7:0]*data_in_10[7:0])

Column two = column two cascade_data_in[63:32] + 32-bit fixed-point of (data_in_1[7:0]*data_in_1[7:0] + data_in_2[7:0]*data_in_2[7:0] + data_in_3[7:0]*data_in_3[7:0] + data_in_4[7:0]*data_in_4[7:0] + data_in_5[7:0]*data_in_5[7:0] + data_in_6[7:0]*data_in_6[7:0] + data_in_7[7:0]*data_in_7[7:0] + data_in_8[7:0]*data_in_8[7:0] + data_in_9[7:0]*data_in_9[7:0] + data_in_10[7:0]*data_in_10[7:0])

Column two = column two col_two_accresult[31:0]+ 32-bit fixed-point of (data_in_1[7:0]*data_in_1[7:0] + data_in_2[7:0]*data_in_2[7:0] + data_in_3[7:0]*data_in_3[7:0] + data_in_4[7:0]*data_in_4[7:0] + data_in_5[7:0]*data_in_5[7:0] + data_in_6[7:0]*data_in_6[7:0] + data_in_7[7:0]*data_in_7[7:0] + data_in_8[7:0]*data_in_8[7:0] + data_in_9[7:0]*data_in_9[7:0] + data_in_10[7:0]*data_in_10[7:0])

Table 28.  Tensor Fixed-point Mode Equations for Data Input Feed Method
Input Operands Cascade Input Enabled Accumulator Enabled
10-element signed 8x8

Column One = column one cascade_data_in[31:0] + 32-bit Fixed-point of (data_in_1[7:0]*b1 + data_in_2[7:0]*b2 + data_in_3[7:0]*b3 + data_in_4[7:0]*b4 + data_in_5[7:0]*b5 + data_in_6[7:0]*b6 + data_in_7[7:0]*b7 + data_in_8[7:0]*b8 + data_in_9[7:0]*b9 + data_in_10[7:0]*b10)

b1, b2, b3, b4, b5 are feed in by shifting data[87:80]

b6, b7, b8, b9, b10 are feed in by shifting data[95:88]

Column One = column one col_one_accresult[31:0] + 32-bit Fixed-point of (data_in_1[7:0]*b1 + data_in_2[7:0]*b2 + data_in_3[7:0]*b3 + data_in_4[7:0]*b4 + data_in_5[7:0]*b5 + data_in_6[7:0]*b6 + data_in_7[7:0]*b7 + data_in_8[7:0]*b8 + data_in_9[7:0]*b9 + data_in_10[7:0]*b10)

b1, b2, b3, b4, b5 are feed in by shifting data[87:80]

b6, b7, b8, b9, b10 are feed in by shifting data[95:88]

Column Two = column two cascade_data_in[63:32] + 32-bit fixed-point of (data_in_1[7:0]*b1 + data_in_2[7:0]*b2 + data_in_3[7:0]*b3 + data_in_4[7:0]*b4 + data_in_5[7:0]*b5 + data_in_6[7:0]*b6 + data_in_7[7:0]*b7 + data_in_8[7:0]*b8 + data_in_9[7:0]*b9 + data_in_10[7:0]*b10)

b1, b2, b3, b4, b5 are feed in by shifting data[87:80]

b6, b7, b8, b9, b10 are feed in by shifting data[95:88]

Column Two = column two col_two_accresult[31:0] + 32-bit fixed-point of (data_in_1[7:0]*b1 + data_in_2[7:0]*b2 + data_in_3[7:0]*b3 + data_in_4[7:0]*b4 + data_in_5[7:0]*b5 + data_in_6[7:0]*b6 + data_in_7[7:0]*b7 + data_in_8[7:0]*b8 + data_in_9[7:0]*b9 + data_in_10[7:0]*b10)

b1, b2, b3, b4, b5 are feed in by shifting data[87:80]

b6, b7, b8, b9, b10 are feed in by shifting data[95:88]

Figure 55. Tensor Fixed-point Mode One Column Datapath
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