Embedded - Microprocessors

Part Number	Description / PDF	Quantity
GWIXP460BAB Intel	RISC MPU, 32-BIT, 266MHZ PBGA544	1984
RK80532PE056512 Intel	MPU, 32-BIT, 2400MHZ CPGA478	21384
NK80530VZ800256 Intel	MOBILE CELERON PROCESSOR	203
RJ80535LC0051M Intel	RISC MPU, 64-BIT	1882
HH80556JJ0534M Intel	INTEL LOW VOLTAGE XEON 5148	1599
TT8050266133 Intel	MICROPROCESSOR, 32-BIT, 133MHZ	15997
NE80546KF1071MM Intel	LOW VOLTAGE INTEL XEON PROCESSOR	2953
JM80547PG0961M Intel	RISC MPU, 32-BIT	225
RH80530WZ006256 Intel	TUALITAN PENTIUM PROCESSOR	5859
LF80539GE0362ME Intel	MPU CORE DUO PROCESSOR T2350	7920
KC80526LY400128 Intel	MPU, 32-BIT, 400MHZ PBGA495	15373
NG80960JF33 Intel	RISC MPU, 32-BIT, 33MHZ PQFP132	2465
5962-8850101YA Intel	MICROPROCESSOR, 16-BIT	2
UM80536UC600512 Intel	CELERON PROCESSOR	16753
TA80960CF25 Intel	RISC MPU, 32-BIT, 25MHZ CPGA168	152
MQ801866 Intel	RISC MPU, 16-BIT	2
NE80546RE072256 Intel	MPU, 64-BIT, 2800MHZ CPGA478	3249
KC80524KX333256 Intel	MPU, 32-BIT, 333MHZ PBGA615	6814
RJ80530GZ004512 Intel	MPU, 32-BIT, 1066MHZ PBGA479	3751
RJ80530GZ009512 Intel	MPU, 32-BIT, 1200MHZ PBGA479	5357

Embedded - Microprocessors

1. Overview

Embedded microprocessors are specialized computing units designed for dedicated control, monitoring, or processing tasks within electronic systems. Unlike general-purpose CPUs, they integrate processing cores, memory interfaces, and peripheral controllers into a single chip (SoC) to optimize performance, power efficiency, and cost for specific applications. These devices form the backbone of modern IoT, automotive systems, industrial automation, and consumer electronics.

2. Main Types and Functional Classification

Type	Functional Characteristics	Application Examples
ARM Cortex-M Series	Low-power 32-bit cores with real-time capabilities, optional DSP extensions	Smart sensors, wearables, motor control
PowerPC	High reliability, floating-point units, automotive/Aerospace focused	Vehicle ECUs, avionics systems
MIPS	Efficient pipelining, 32/64-bit variants for multimedia processing	Networking equipment, set-top boxes
RISC-V	Open instruction set, modular architecture for customization	AI accelerators, edge computing devices
x86 (Embedded)	PC compatibility, high processing power with integrated GPUs	Industrial PCs, medical imaging equipment

3. Architecture and Components

A typical embedded microprocessor contains:

Processing Core(s): RISC/Complex Instruction Set architectures with 1-8 cores
Memory Subsystem: Integrated SRAM, ROM, and external DRAM controllers
Peripheral Interfaces: UART, SPI, I2C, USB, CAN, PCIe, Ethernet MAC
Real-Time Components: Watchdog timers, PWM generators, ADC/DAC modules
Power Management: Multiple sleep modes, DVFS (Dynamic Voltage/Frequency Scaling)

4. Key Technical Specifications

Parameter	Description	Importance
Clock Frequency	Operating speed (10MHz-6GHz)	Determines processing throughput
Instruction Set	RISC vs CISC architecture	Affects code density and power consumption
TDP (Thermal Design Power)	Power consumption under load (100mW-50W)	Dictates thermal management requirements
Process Node	Manufacturing technology (28nm-5nm)	Impacts performance and energy efficiency
Memory Bandwidth	Data transfer rate between core and memory	Limits performance in data-intensive tasks

5. Application Domains

Consumer Electronics: Smartphones (application processors), home appliances
Automotive: ADAS controllers, engine management systems
Industrial: PLCs (Programmable Logic Controllers), robotics
Healthcare: MRI scanners, portable diagnostic devices
Communications: 5G base stations, optical network transceivers

6. Leading Manufacturers and Products

Manufacturer	Representative Product	Key Features
ARM Holdings	Cortex-A78	128-bit NEON engine, 4nm process, 3.0GHz
Intel	Atom x6425E	Quad-core, 12W TDP, integrated Intel HD Graphics
NXP Semiconductors	i.MX 8M Plus	1.8GHz Cortex-A53, NPU for ML acceleration
Microchip	SAM9X60	32-bit ARM926EJ-S, 120MHz, ECC memory support
Qualcomm	QCS610	Hexagon DSP, Adreno GPU, AI Engine for computer vision

7. Selection Guidelines

Key considerations include:

Performance requirements vs power budget (e.g., Cortex-M55 for ultra-low-power AI)
Real-time constraints (deterministic latency for motor control applications)
Peripheral integration (CAN FD for automotive networks)
Software ecosystem (RTOS support, middleware availability)
Longevity and supply chain stability (automotive-grade qualification)

Case Study: A smart thermostat design selected NXP's MCIMX7U5 (Cortex-M4 + Cortex-A7) for its combination of real-time sensor processing and application-layer connectivity.

8. Industry Trends

Emerging directions include:

AI integration: On-chip neural processing units (NPUs) for edge ML inference
Heterogeneous computing: Multi-architecture SoCs (RISC-V + GPU + NPU)
Advanced packaging: 3D-stacked memory integration for bandwidth-intensive applications
Functional safety: ISO 26262 compliance for autonomous vehicle systems
Open ecosystems: Growth of RISC-V adoption in custom ASIC designs